The Quantum-Algorithmic Revolution: Pioneering First-Fundamentals AI for Understanding Complex Adaptive Systems

Co-created by the Catalyzer Think Tank divergent thinking and Gemini Deep Research tool.

 

I. Executive Summary

This report analyzes the evolving landscape of “quantum deep tech” and its role in enabling a new generation of Artificial Intelligence—”first-fundamentals AI.” This AI paradigm, grounded in mathematical proof and causal understanding, seeks to move beyond correlational observations (akin to a single-layer observation like PV=nRT) to discover the objective, underlying mechanisms of complex adaptive systems (CAS). The analysis spans major global technology corporations—Apple, Google/Alphabet, Microsoft, Cisco, Nvidia, Broadcom, AMD, Intel, ARM, Samsung, LG, NEC, Panasonic, Toshiba, Hitachi, NTT—and their Chinese competitors. It examines their strategies, research and development (R&D) efforts, and investments from 2000 to the present, drawing on annual reports, news, and research publications.

The core of this investigation lies in how these entities are developing and integrating quantum computing, advanced AI methodologies (such as physics-informed AI, neuro-symbolic AI, and causal AI), and sophisticated sensing technologies to model and understand systems from fundamental material and molecular perspectives up to observable phenomena and personalized interactions. The pursuit is not merely incremental improvement but a fundamental shift towards AI systems that can reason from first principles, discover objective truths within CAS, and potentially predict or influence their behavior with unprecedented accuracy. Chinese entities, supported by national strategic initiatives, are rapidly advancing in both quantum technologies and AI, creating a dynamic and competitive global environment. The report synthesizes these multifaceted efforts, highlighting distinct corporate strategies, technological trajectories, and the profound implications for scientific discovery, technological innovation, and societal development.

II. Defining the Core Concepts: Quantum Deep Tech and First-Fundamentals AI for CAS Objectivity

The convergence of quantum technologies and advanced AI is paving the way for a new era of scientific discovery and technological innovation. At the heart of this transformation are “quantum deep tech” and the pursuit of “first-fundamentals AI,” both aimed at achieving an objective understanding of complex adaptive systems (CAS).

Quantum Deep Tech: This term refers to companies and technologies built on fundamental principles of engineering and science, often requiring significant capital, time, and R&D investment.1 In the context of this report, it encompasses areas like advanced semiconductor development, quantum computing (hardware and algorithms), quantum sensing, and quantum-secure communication, all of which are foundational to the next wave of technological advancement.1 These technologies are not just incremental improvements but offer novel solutions to complex global challenges by leveraging the principles of quantum mechanics.2

First-Principles AI (or First-Fundamentals AI): This is a revolutionary approach to artificial intelligence that diverges from traditional AI’s reliance on existing models and incremental improvements. Instead, First-Principles AI deconstructs problems to their fundamental truths and reconstructs solutions from scratch.3 This often involves creating a system model, which might be a multivariate generative (probabilistic) time-series predictor, combined with online planning or learning model-free agents acting on this system model.4 The objective is to build AI systems that can reason from foundational knowledge, akin to how scientists derive theories from basic laws, rather than just learning patterns from observational data. Such an AI would aim to understand the “why” behind phenomena, not just the “what.”

Complex Adaptive Systems (CAS): Many real-world systems, from economies and societies to biological organisms and the climate, are complex adaptive systems. These systems are characterized by a large number of interacting components (agents) whose collective behavior leads to emergent properties, non-linear dynamics, and adaptation over time.5 AI itself, particularly Generative AI (GenAI), can be conceptualized as a socio-technological CAS, where people, policies, data, and algorithms co-evolve.5 Understanding and modeling CAS is a grand challenge, as their behavior cannot always be predicted by analyzing their components in isolation.

Discovering Objectivity in CAS: The user query emphasizes moving beyond a “single layer of observation” (like the empirical gas law PV=nRT) to discover the objective reality of CAS. This implies a shift from purely correlational AI models to those that can uncover causal relationships and the underlying mechanisms governing system behavior. First-fundamentals AI, by its nature, aims for this deeper, objective understanding. It seeks to build models based on verifiable principles, potentially mathematical proofs or fundamental physical laws, to explain and predict the omnipresent nature of matter and its interactions from molecular to observable scales. This also extends to understanding how these systems manifest personalized experiences or perspectives.

The integration of quantum deep tech is crucial here. Quantum computers promise to simulate quantum systems from first principles, offering unprecedented insight into molecular interactions and material properties.6 Quantum sensors can provide highly precise data about the physical world at fundamental levels.8 This data and computational capability can then feed into first-principles AI models, enabling them to build more accurate and objective representations of CAS. Advanced AI paradigms like Physics-Informed AI (integrating physical laws into machine learning) 10, Neuro-Symbolic AI (combining neural learning with symbolic reasoning) 12, and Causal AI (focusing on cause-and-effect relationships) 14 are key methodologies in this pursuit.

III. Leading Western Technology Companies: Strategies and Deep Tech Initiatives

The quest for quantum deep tech and first-fundamentals AI is being vigorously pursued by leading Western technology corporations. Their approaches vary, reflecting their core competencies, strategic priorities, and long-term visions. This section details the efforts of Apple, Google/Alphabet, and Microsoft, examining their investments in quantum computing, AI for scientific discovery, fundamental AI research, and technologies for understanding complex adaptive systems.

A. Apple Inc. (AAPL)

Apple’s engagement with quantum deep tech and advanced AI, particularly concerning first-principles understanding, appears more nascent or less publicly detailed compared to some other tech giants, based on available information from 2000 to the present. However, their strategic interests and research directions provide clues to their potential trajectory.

1. Quantum Computing Research and Strategy:

* What & How: Apple is reportedly investing in quantum technology, with a significant focus on quantum-safe encryption methods.16 This initiative aims to ensure the security of Apple’s vast ecosystem as quantum computing capabilities advance and potentially threaten current cryptographic standards. Direct, large-scale public announcements regarding the development of general-purpose quantum computing hardware or extensive quantum algorithm research for simulations are less prominent. The term “Quantum Strategy” has been associated with Apple in business literature, but often metaphorically, referring to their ability to balance operational efficiency with serial innovation, rather than direct quantum computing R&D.17

* Why: The primary driver for Apple’s quantum interest, visible in public-facing information, appears to be long-term security and future-proofing its products and services against threats posed by quantum computers.16 Maintaining user trust through robust privacy and security is a cornerstone of Apple’s brand.

* Relevance to First Fundamentals AI: Currently, Apple’s quantum efforts seem more geared towards mitigating the risks of quantum computing (cryptography) rather than directly leveraging quantum computation to enable first-fundamentals AI for scientific discovery. However, a secure foundation is essential for any future advanced computational endeavors.

2. AI for Scientific Discovery and Fundamental AI Research:

* What & How: Apple conducts significant fundamental research in machine learning (ML) and AI. Their researchers present work at top conferences like ICLR, covering areas such as visual understanding, generative AI, reasoning, instruction-following, uncertainty, efficiency, attention mechanisms, and optimization.18 A specific example is “Depth Pro,” a model for sharp monocular metric depth estimation from 2D images, surpassing prior limitations in resolution and reliance on camera intrinsics.18 While there are general discussions about “AI for Science and Discovery” in the broader tech ecosystem 19, specific large-scale Apple initiatives to use AI to discover fundamental laws of nature (e.g., in physics or chemistry) are not heavily publicized in the provided materials.

* Why: Apple’s AI research primarily aims to enhance its product capabilities, improve user experiences, and redefine what’s possible with its devices and services.18 Fundamental research in areas like reasoning and generative AI underpins these product-focused innovations.

* Relevance to First Fundamentals AI & Math Proof: Apple’s research into AI reasoning and generative models could lay groundwork for more fundamental AI capabilities. If AI systems can achieve sophisticated reasoning, this could eventually be applied to scientific problems or even assist in formal mathematical proofs. However, based on the available information, this does not appear to be the primary, explicitly stated goal of their current AI for science efforts. There is no explicit mention in the provided snippets of Apple pursuing physics-informed AI, neuro-symbolic AI for scientific discovery from first principles, causal AI for uncovering fundamental laws, or complex adaptive system modeling from a matter/molecular perspective to discover objectivity. Their AI seems focused on observable perspectives and personalization for product features.

3. Approach to Complex Adaptive Systems and Personalization:

* Apple’s focus on user experience and tightly integrated hardware-software ecosystems inherently involves managing complex systems and delivering personalized experiences. Their AI research likely contributes to understanding user behavior (an observable CAS) and tailoring device interactions. However, the depth of this research in terms of modeling CAS from first principles of matter or molecular interactions to achieve objective understanding is not detailed in the provided information.

Apple’s strategy, based on the available data, appears to prioritize leveraging AI for immediate product innovation and user experience enhancements, while their quantum interests are currently centered on the vital area of future-proofing security. While their fundamental AI research in reasoning is a necessary component for any advanced AI, a direct, publicly articulated push towards using quantum deep tech to build first-fundamentals AI for discovering objective truths in complex adaptive systems from molecular or mathematical-proof standpoints is not strongly evident in the provided materials from 2000 to present. Their efforts are more aligned with improving observable perspectives and delivering sophisticated personalization.

B. Google / Alphabet Inc. (GOOGL)

Alphabet, through Google Research and Google DeepMind, has established itself as a leader in both AI and quantum computing research, with a clear trajectory towards tackling fundamental scientific problems and developing more capable AI systems. Their 2024 Form 10-K report emphasizes their transition to an “AI-first company” and continued long-term investment in frontier technologies like quantum computing.20

1. Quantum Computing Research and Strategy (Google Quantum AI):

* What & How: Google Quantum AI aims to build a large-scale, fault-tolerant quantum computer.21 Their hardware approach focuses on superconducting transmon qubits, exemplified by processors like Sycamore (54 qubits, used for the 2019 quantum supremacy demonstration 22) and the more recent Willow chip.24 Google’s roadmap includes milestones for achieving logical qubits and scaling error-corrected machines.21 A key part of their strategy involves overcoming materials science defects, improving qubit coherence, and developing scalable cryogenic systems, often through collaborations with academia.21 The Quantum AI Lab (QuAIL), a joint initiative with NASA and USRA, has been central to these efforts.24

* Why: The primary goal is to solve complex problems that are intractable for classical supercomputers.7 This includes fundamental scientific research, materials science, drug discovery, and potentially advancing AI itself. The 2024 10-K report lists quantum computing as a key area of future investment alongside AI applications.20

* Relevance to First Fundamentals AI: Quantum computers are seen as essential tools for simulating quantum mechanical systems from first principles. This capability is crucial for understanding matter at a fundamental level (molecular and sub-atomic) and could provide the data and insights needed to build AI models that reason from these first principles. For instance, simulating chemical reactions or designing new materials with desired properties requires this level of quantum simulation.

* Snippet Integration: 7 (Quantum principles), 21 (Google Quantum AI research strategy), 22 (Quantum supremacy experiment), 23 (Sycamore processor), 24 (Quantum AI Lab, Willow chip), 20 (Alphabet 2024 10-K).

2. AI for Scientific Discovery (Google DeepMind & Google Research):

* What & How: Google has a strong focus on using AI to accelerate scientific breakthroughs across disciplines.25

* Biology and Medicine: AlphaFold (now AlphaFold 3) is a landmark achievement, predicting protein structures and interactions of all life’s molecules with high accuracy.20 This has profound implications for drug discovery, vaccine development, and understanding diseases. The “AI Co-Scientist,” built on Gemini 2.0, is a multi-agent AI system designed to assist researchers in generating novel hypotheses and research plans in biomedical sciences, including drug repurposing and target discovery.26 Other projects include AMIE for medical diagnostic conversations and REGLE for genomics research.27

* Chemistry and Materials Science: GNoME (Graph Networks for Materials Exploration) has discovered millions of new stable inorganic crystal structures, crucial for batteries, solar panels, and chips.25

* Physics and Geospatial Science: AI models are used for medium-range weather forecasting (outperforming traditional methods), climate change prediction (NeuralGCM), mapping the ionosphere using smartphone data, and wildfire tracking.25 “Geospatial Reasoning” aims to combine geospatial foundation models with generative AI for conversational data exploration.27

* Mathematics: AlphaGeometry combines a neural language model with a rule-bound deduction engine to solve complex Olympiad-level geometry problems, demonstrating AI’s growing ability for logical reasoning and verifying new knowledge.25

* Why: To address fundamental scientific questions, push the boundaries of knowledge, and develop novel solutions to global challenges in health, sustainability, and basic science.25

* Relevance to First Fundamentals AI & Math Proof:

* AlphaFold and GNoME derive understanding from fundamental properties (amino acid sequences, atomic arrangements) to predict complex structures (proteins, crystals).20 This is a step towards AI understanding molecular and material systems from their basic constituents.

* AlphaGeometry’s success in solving geometry problems using a neuro-symbolic approach that involves logical deduction is directly relevant to “AI based on math proof”.30 It shows AI’s potential to reason from axioms and established rules.

* The AI Co-Scientist aims to generate novel, evidence-backed hypotheses 26, moving beyond pattern recognition to a form of scientific reasoning.

* Snippet Integration: 26 (AI Co-Scientist), 25 (DeepMind AI for science overview), 30 (AlphaGeometry), 28 (Physics-aware flood modeling), 27 (Google Research scientific discovery blog), 20 (AlphaFold in 10-K), 26 (AI Co-Scientist), 25 (DeepMind projects), 30 (AlphaGeometry details).

3. Fundamental AI Research: Physics-Informed, Neuro-Symbolic, Causal AI, and CAS Modeling:

* Physics-Informed AI (PINN): Google Research has published on physics-aware deep learning, for example, in scalable flood modeling where neural networks are trained to optimize coarse grid representations so flood predictions match fine-grid, physics-based simulations.28 They also fund academic research in physics-informed ML for applications like wildfire pollutant transport forecasting.31 General research on PINNs is active.32

* Snippet Integration: 28 (Physics-aware flood modeling), 31 (Funded research).

* Neuro-Symbolic AI (NSAI): Google has a history of research in this area. “Neural Symbolic Machines” (2017) proposed integrating neural networks with symbolic Lisp program execution for semantic parsing on knowledge bases like Freebase.38 More recent work explores adaptable and interpretable neural memory over symbolic knowledge (fact memory).39 AlphaGeometry is a prime example of a successful neuro-symbolic system.30 The broader field is rapidly growing.40

* Snippet Integration: 30 (AlphaGeometry), 39 (Neural memory over symbolic knowledge), 38 (Neural Symbolic Machines).

* Causal AI & Causal Inference: Google Research actively publishes on causal inference methods, such as techniques for combining randomized trials and observational studies to improve generalizability and treatment effect estimation 44, and comparing methods for estimating sales lift.45 DeepMind has researched how robust agents learn causal world models, hypothesizing that learning causal models is fundamental for generalization under distributional shifts.46 Google also invests in and applies Causal AI in areas like online advertising and healthcare, aiming for more accurate, reliable, and fair AI systems.47

* Snippet Integration: 44 (Combining RCTs and observational data), 46 (Robust agents learn causal world models), 45 (Causal inference for sales lift), 47 (Google’s Causal AI applications).

* AI for Modeling Complex Adaptive Systems (CAS): Google’s work on large-scale simulations (weather, climate 25), understanding the ionosphere 25, and the behavior of LLMs themselves 5 touches upon modeling CAS. Research on extending ML abstraction to incorporate societal context using CAS theory and system dynamics 49 directly addresses this. DeepMind’s work on emergent behaviors in multi-agent systems (e.g., in games) also relates to CAS.51 The SYMBIOSIS framework aims to make systems thinking accessible for societal challenges using AI, underscoring AI’s role in understanding CAS characteristics like feedback loops and time delays.52

* Snippet Integration: 25 (Weather, climate, ionosphere), 52 (SYMBIOSIS), 51 (Emergent behavior in agents), 49 (CAS for societal context in ML).

* Personalization AI & Molecular Perspectives: Gemini’s integration across Google’s billion-user products (Search, Maps, Android, etc.) inherently involves personalization at a massive scale.20 AlphaFold 3’s ability to predict interactions of “all the molecules in life’s processes” 20 directly addresses molecular perspectives.

4. Quantum Sensing Initiatives:

* What & How: Google Quantum AI, in collaboration with UConn and NORDITA, has studied the effects of gravitation on quantum information systems, demonstrating that qubits can act as precise gravity sensors.54 This research suggests future quantum chips could serve dual purposes: computation and sensing. The Google Sycamore chip, with its vertically aligned qubits, was noted as a relevant architecture for such effects.54

* Why: To explore new applications of quantum technology beyond computation, potentially leading to breakthroughs in areas like GPS-free navigation.54 This also contributes to a deeper understanding of qubit behavior and environmental interactions, which is crucial for building better quantum computers.

* Relevance to First Fundamentals AI: Highly sensitive quantum sensors can provide unprecedented data about fundamental physical phenomena. This data could be used to validate or train AI models that aim to understand these phenomena from first principles. For example, precise measurements of gravitational fields or other fundamental forces could refine physical theories and the AI models built upon them.

* Snippet Integration: 54 (Google/UConn quantum gravity sensing).

5. Stated Objectives and Pursuit of Objectivity/Fundamental Understanding:

* Google’s mission includes organizing the world’s information and making it universally accessible and useful. In the AI era, this extends to generating understanding and insights.

* The development of Gemini as a natively multimodal model capable of generalizing and seamlessly understanding and combining different information types points to a drive for more fundamental AI understanding.20

* Projects like AlphaFold, AlphaGeometry, and the AI Co-Scientist explicitly aim to solve fundamental scientific problems and generate new knowledge, often by building models that reflect underlying principles or employ logical reasoning.20

* The research into causal world models 46 and CAS for societal context 49 directly targets a more objective, mechanism-based understanding rather than just correlational patterns.

* The 2024 10-K report highlights the development of Gemini and its iterations, and projects like Astra (universal AI assistant) and Mariner (AI agent for Chrome), indicating a push towards more general and capable AI.20

The work at Google and Alphabet points to a comprehensive strategy. They are developing powerful quantum computers that could simulate systems from first principles, providing foundational data. Simultaneously, they are advancing AI for scientific discovery (AlphaFold, GNoME, AI Co-Scientist) and fundamental AI methodologies (neuro-symbolic with AlphaGeometry, causal AI, CAS modeling). This dual approach suggests a long-term vision where quantum-enhanced simulations and AI-driven discovery from fundamental principles converge to create a new paradigm of understanding complex systems. The ability of AlphaGeometry to solve mathematical problems with verifiable proofs, and the aim of Causal AI to distinguish causation from correlation, are strong indicators of a push towards AI systems that can establish objective truths.

The development of the AI Co-Scientist 26, which uses Gemini 2.0 to generate novel, evidence-backed hypotheses for biomedical research, exemplifies this. By having AI agents for generation, reflection, ranking, and evolution, overseen by a supervisor, it mimics a structured scientific inquiry process. Validating AI-generated hypotheses in lab experiments for drug repurposing and target discovery demonstrates a tangible link between advanced AI reasoning and real-world scientific validation. This iterative process of hypothesis generation and experimental feedback is crucial for building AI that doesn’t just interpolate known data but actively contributes to discovering new, objective knowledge.

Furthermore, Google’s research into making systems thinking more accessible via AI-powered frameworks like SYMBIOSIS 52 shows an intent to equip AI with the tools to understand the broader characteristics of complex adaptive systems, such as feedback loops and non-linearities. This is essential if AI is to move beyond isolated problem-solving to understanding the interconnected nature of real-world phenomena from a more holistic and objective viewpoint.

C. Microsoft Corporation (MSFT)

Microsoft is making substantial investments across the AI and quantum landscape, with a clear strategy to integrate these technologies into its cloud services (Azure) and product ecosystem, and to drive scientific discovery. Their approach involves building a scalable quantum computer, developing AI tools for science, and fostering research in fundamental AI paradigms. The 2023 and 2024 Annual Reports highlight AI as a defining technology, with Microsoft aiming to reimagine every layer of its tech stack for the AI era.55

1. Quantum Computing Research and Strategy (Azure Quantum, Topological Qubits):

* What & How: Microsoft’s primary quantum hardware effort centers on developing topological qubits, leveraging so-called “Majorana quasiparticles”.59 They believe this approach offers a path to more stable, error-resistant qubits that are easier to scale to the millions needed for fault-tolerant quantum computing. The Majorana 1 chip is a key milestone, demonstrating a topological core architecture.60 Microsoft Azure Quantum is their cloud platform, providing access to diverse quantum hardware from partners and their own future systems, along with a comprehensive software stack including the Quantum Development Kit (QDK) and the Q# language.63 They emphasize a full-stack approach from qubits to applications and engage in strategic collaborations (e.g., with Quantinuum, Atom Computing for logical qubit demonstrations).62 Their topological approach has been vetted by DARPA’s US2QC program.60

* Why: To build a scalable, fault-tolerant quantum supercomputer capable of solving some of humanity’s hardest problems, particularly in chemistry and materials science.59 The goal is to accelerate scientific discovery and enable practical quantum applications significantly sooner than other approaches might allow.59

* Relevance to First Fundamentals AI: Microsoft’s quantum computing efforts aim to provide the computational power necessary to simulate quantum systems from first principles. Azure Quantum Elements 63 is specifically designed to leverage this for chemistry and materials science R&D, suggesting a direct route to using quantum insights for building more fundamental, physics-based AI models. The ability to perform accurate quantum simulations is a cornerstone for an AI that can understand matter at its most fundamental level.

* Snippet Integration: 59 (Quantum research strategy), 63 (Microsoft Quantum mission), 64 (Azure Quantum overview), 60 (Majorana 1 chip, topological qubits), 65 (Quantum computing publications), 61 (Majorana chip), 62 (Topological approach, collaborations).

2. AI for Scientific Discovery: Azure Quantum Elements and Specialized Applications:

* What & How: Azure Quantum Elements (AQE) is Microsoft’s flagship platform for accelerating scientific discovery in chemistry and materials science. It integrates AI, High-Performance Computing (HPC), and quantum computing capabilities.63 Microsoft’s AI for Science initiative supports broader research, providing infrastructure and AI tools to biologists (e.g., for genetic marker identification, drug development), chemists (e.g., predicting molecular properties, designing novel compounds through generative chemistry), and physicists (e.g., quantum mechanics simulations, fusion energy research).66

* Why: To accelerate scientific breakthroughs, enable the discovery of new materials with desired properties, optimize chemical reactions, speed up the development of vaccines and drugs, and tackle grand challenges like achieving controlled nuclear fusion.66 The strategy emphasizes empowering the scientific community with advanced AI-driven tools and platforms.

* Relevance to First Fundamentals AI & Math Proof:

* AQE’s combination of quantum simulation capabilities with AI directly supports building models of materials and chemical reactions from the fundamental laws of quantum mechanics. This is a clear path towards “first fundamentals” understanding in these domains.

* “Generative Chemistry” 66 aims to design novel molecules, potentially by learning and applying fundamental principles of chemical bonding and interaction.

* The use of physics-informed AI for simulating materials and plasma behavior in fusion research 67 explicitly incorporates known physical laws into AI models, enhancing their predictive power and interpretability.

* Snippet Integration: 66 (AI for Scientific Discovery vision), 67 (Fusion Summit, AI for plasma/materials), 63 (Azure Quantum Elements).

3. Fundamental AI Research: Physics-Informed, Neuro-Symbolic, Causal AI, and CAS Modeling:

* Physics-Informed AI: Microsoft Research is actively engaged in physics-informed AI, particularly for simulating complex physical systems like materials and plasma in fusion energy research.67 This involves embedding physical laws within AI models to guide learning and improve predictions.

* Snippet Integration: 67 (Fusion research).

* Neuro-Symbolic AI (NSAI): Microsoft Research has dedicated projects focusing on neuro-symbolic computation, particularly for inference reasoning in Natural Language Processing (NLP) and multimodal applications.68 They have also explored neuro-symbolic approaches for visual reasoning, aiming to disentangle visual perception from symbolic reasoning steps.69 Their general AI development, including tools like Copilot built on LLMs, often involves components (neural networks for pattern recognition, and some level of structured output or interaction) that are conceptually related to the goals of NSAI, even if not always explicitly labeled as such.55

* Snippet Integration: 69 (Neuro-Symbolic Visual Reasoning), 68 (Neuro-Symbolic Computation in NLP), 70 (AI toolkit, generative AI).

* Causal AI & Causal Inference: Microsoft has a significant and well-structured initiative in Causal AI. They have developed a Causal AI Suite of open-source tools, including DoWhy (for modeling assumptions and guiding causal analysis through model, identify, estimate, refute steps), EconML (providing advanced machine learning methods for estimating individualized causal responses and heterogeneous treatment effects), Causica (focusing on deep learning methods for end-to-end causal inference and discovery, using approaches like additive noise structural equation models), and ShowWhy (a no-code GUI for domain experts).71 The stated goal is to broaden the accessibility and practical usage of causal methods and to accelerate “use-inspired basic research” in causality, moving beyond mere correlations to understand cause-and-effect relationships.

* Snippet Integration: 71 (Detailed Causal AI Suite).

* AI for Modeling Complex Adaptive Systems (CAS): Microsoft’s research touches on CAS modeling from several angles. Their work in AI for fusion research inherently involves modeling plasma, a highly complex adaptive system.67 The development of the Trace framework for end-to-end optimization and dynamic adaptation of AI agents treats AI systems themselves as computational graphs that can change and adapt, relevant for multi-agent systems.72 Research into multi-agent systems and AI-powered problem-solving frameworks that facilitate dynamic engagement and diverse interactions also aligns with CAS principles.73 The “Societal AI” initiative aims to build human-centered AI systems that reflect shared values, which requires understanding and navigating complex societal dynamics.73 General publications also discuss CAS theory in contexts like climate change and health 76 or organizational theory 78, indicating broader awareness.

* Snippet Integration: 67 (Fusion plasma modeling), 224 (Multiple model approach), 73 (Multi-agent systems, Societal AI), 72 (Trace framework), 76 (CAS in climate/health context).

* Personalization AI & Molecular Perspectives: Azure Quantum Elements is explicitly designed for molecular-level understanding in chemistry and materials science.63 Microsoft’s extensive suite of AI-powered products, including Microsoft 365 Copilot, aims to deliver personalized productivity and user experiences.55

4. Quantum Sensing Initiatives:

* What & How: While Microsoft’s primary quantum hardware focus is on topological qubits for computation, their strategic documents acknowledge quantum sensing as one of the three pillars of quantum technology (computing, communication, sensing).59 The Azure Quantum platform 64 is an open ecosystem, which could potentially integrate quantum sensing data or capabilities in the future, especially for scientific discovery through Azure Quantum Elements. Microsoft Research has also explored advanced sensing technologies like metasurfaces for wireless applications, which, while not strictly “quantum sensing” in the atomic manipulation sense, push the boundaries of precision measurement.75 The broader context of U.S. government interest in quantum sensing for various applications (navigation, medical, environmental) is also relevant.54

* Why: To leverage the extreme precision of quantum sensors for fundamental scientific discovery and advanced applications. This could involve characterizing quantum materials (including their own qubits) or using sensor data to inform AI models.

* Relevance to First Fundamentals AI: High-fidelity data from quantum sensors could provide the detailed observational inputs necessary to train and validate AI models that aim for a fundamental, objective understanding of physical and biological systems.

* Snippet Integration: 59 (Mention of quantum sensing as a key quantum technology area), 75 (Metasurface research for advanced sensing).

5. Stated Objectives and Pursuit of Objectivity/Fundamental Understanding:

* Microsoft’s mission is “to empower every person and every organization on the planet to achieve more”.55 In the AI era, this includes providing tools that enhance reasoning and insight generation.

* The company aims to “accelerate scientific discoveries for a better future” through its quantum program.63

* The Causal AI suite is explicitly designed to move beyond correlations to uncover cause-and-effect relationships, which is fundamental to achieving an objective understanding of systems.71

* The pursuit of topological qubits represents a high-risk, high-reward endeavor aimed at building truly scalable quantum computers necessary for tackling fundamental scientific problems.60

* Azure Quantum Elements’ focus on chemistry and materials science 63 indicates a drive towards understanding and designing matter from fundamental quantum mechanical principles.

* Microsoft’s annual reports (2023: 55; 2024: 58) emphasize a “new era of AI” featuring powerful reasoning engines. They are committed to infusing generative AI capabilities across their entire technology stack, guided by responsible AI principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.55

Microsoft’s approach can be characterized as building a comprehensive ecosystem of tools and platforms (Azure Quantum, Causal AI suite) while making a focused, long-term bet on topological qubits for scalable quantum hardware. This pragmatic strategy aims to empower both internal and external researchers to tackle complex scientific problems, particularly in materials science and chemistry, where quantum mechanics is fundamental. Their Causal AI initiative directly addresses the need for AI to understand objective, cause-and-effect relationships.

A notable aspect of Microsoft’s strategy is the dual perspective on AI: AI as a tool for scientific discovery and understanding external complex systems (e.g., fusion plasma, new materials), and AI systems themselves (especially multi-agent and LLM-based systems) as complex adaptive systems that need to be understood, designed, and managed. Research into self-adapting AI agents (Trace framework 72) and multi-agent systems 73 reflects this internal focus. This reflexivity is crucial; to build AI that can objectively understand external CAS, the AI itself must be robust, interpretable, and its own complex behaviors managed. This connects to their strong emphasis on Responsible AI principles, which are essential for deploying AI that interacts with and shapes complex societal systems.

IV. Other Key Western Technology Companies: Diverse Approaches

Beyond Apple, Google, and Microsoft, several other major Western technology firms are making significant, albeit often more specialized, contributions to the fields of quantum technology and advanced AI. Their strategies reflect their unique market positions and core competencies.

A. Cisco Systems, Inc. (CSCO)

Cisco’s primary contributions to the quantum and advanced AI landscape are centered on building the foundational networking infrastructure for future quantum systems and leveraging AI for network automation and intelligence.

1. Quantum Computing Research and Strategy (Quantum Networking):

* What & How: Cisco is actively developing quantum networking technologies. This includes a prototype entanglement chip designed for scalable, distributed quantum computing by enabling the transmission of quantum information (via entangled photons) over long distances.86 This chip, developed with UC Santa Barbara, operates at telecom wavelengths and room temperature. Cisco has also established Cisco Quantum Labs, a dedicated research facility in Santa Monica focused on the full quantum networking stack. This includes software components like a compiler for distributed quantum systems, a Quantum Network Development Kit (QNDK), and a quantum random number generator (qRNG) based on quantum vacuum noise.86 Their strategy envisions quantum data centers with smaller, networked quantum processors, mirroring the evolution of classical distributed computing. Additionally, Cisco is working on post-quantum cryptography (PQC) to secure classical networks against future quantum threats.86

* Why: To enable scalable and distributed quantum computing by providing the essential communication fabric. Cisco aims to address the challenge of scaling quantum processors beyond a few hundred qubits by connecting multiple smaller QPUs. This also extends to near-term applications of quantum networking principles for secure communications, time synchronization, and location verification on classical systems.86

* Relevance to First Fundamentals AI: Robust quantum networks are a critical “quantum deep tech” enabler. Distributed quantum computing, facilitated by such networks, would be necessary to tackle the extremely large-scale simulations required for first-principles AI models of complex quantum systems (e.g., large molecules, exotic materials). While Cisco isn’t directly building the AI models, they are developing an essential part of the infrastructure that would support such endeavors.

2. AI for Scientific Discovery and Fundamental AI Research (Networking Focus):

* What & How: Cisco’s AI Research team focuses on applying AI, particularly Generative AI, to networking automation, human-computer interaction, and model safety.87 Their goal is to build domain-specific foundational representations relevant to networking. Research includes representation learning and problem-specific properties, fine-tuning foundation models (LLMs, multimodal models) for networking applications. They aim for thought leadership through publications and workshops.87

* Why: To enhance the intelligence, efficiency, and security of networking infrastructure, which is Cisco’s core business. AI can help manage the increasing complexity of global networks and automate operations.

* Relevance to First Fundamentals AI: While the primary application is networking, the research into “domain-specific foundational representations” and combining representation learning with “problem-specific properties” 87 touches upon creating more fundamental and interpretable AI models within a specific domain. If these principles are generalizable, they could contribute to broader first-fundamentals AI. However, direct application to discovering fundamental laws of physics or chemistry is not the stated focus.

Cisco is positioning itself as a key enabler of the future quantum internet and AI-driven network intelligence. Their work on quantum entanglement distribution and the full quantum networking stack is fundamental for the vision of large-scale, distributed quantum computers. Such systems would be indispensable for the kind of complex simulations that first-fundamentals AI for molecular or material systems would require. Thus, Cisco’s contribution is foundational, providing the “quantum plumbing” that other quantum computing and AI efforts will rely upon.

B. Nvidia Corporation (NVDA)

Nvidia’s strategy is pivotal in the quantum and AI domains, not by building quantum computers directly, but by providing the critical accelerated computing hardware and software platforms that empower the entire ecosystem.

1. Quantum Computing Strategy (Enabling Hybrid Systems and Accelerating Research):

* What & How: Nvidia’s approach is to be an accelerated computing company for the quantum era.88 They provide GPUs, the CUDA-Q platform, and the cuQuantum SDK to facilitate hybrid quantum-classical computing. A key initiative is the NVIDIA Accelerated Quantum Center (NVAQC) in Boston, which will integrate quantum hardware from partners with Nvidia’s AI supercomputers (e.g., GB200 NVL72 systems) to develop hybrid quantum algorithms and AI-driven quantum applications.88 They engage in numerous partnerships with quantum hardware companies (e.g., Quantum Circuits, Quantum Machines) and research institutions (e.g., MITRE, QuEra) to optimize quantum simulations, error correction, and algorithm development.88 For instance, their collaboration with QC Design enables GPU-accelerated simulations of over 400 fault-tolerant qubits on a single GPU, and with QuEra, they developed a transformer-based AI decoder for quantum error correction.88

* Why: To accelerate the entire quantum computing field by reducing computational bottlenecks in simulation and control, speeding up error correction research, and enabling the development and deployment of practical hybrid quantum-classical algorithms.88 They aim to foster synergy between classical supercomputing, AI, and quantum processing.

* Relevance to First Fundamentals AI: By providing powerful tools for simulating quantum systems (cuQuantum) and for quantum machine learning (CUDA-Q), Nvidia directly enables researchers working on first-principles models of quantum phenomena. The ability to simulate larger and more complex quantum systems on classical supercomputers, and to design and test quantum algorithms more efficiently, is crucial for progress towards quantum computers that can tackle fundamental scientific discovery. Their AI-driven QEC decoders also highlight the synergy between AI and quantum for building more robust quantum systems.

2. AI for Scientific Discovery and Fundamental AI Research (BioNeMo, Physics-Informed):

* What & How: Nvidia offers BioNeMo, an open-source framework and collection of pre-trained models (e.g., for protein, DNA, RNA sequences like ESM-2, Evo2) specifically tuned for drug discovery and biomolecular research.89 It’s designed for large-scale training and fine-tuning of models for tasks like predicting molecular interactions and designing novel therapeutics. BioNeMo runs on platforms like Google Kubernetes Engine (GKE), showcasing cross-industry collaboration.89 More broadly, Nvidia’s GPUs are the workhorse for training most large-scale AI models used in scientific discovery across various fields, including physics-informed simulations.

* Why: To dramatically accelerate research timelines in critical areas like medicine and materials science by providing specialized AI tools and the underlying computational power.89

* Relevance to First Fundamentals AI: BioNeMo facilitates a data-driven approach to understanding molecular interactions and designing therapeutics from a molecular perspective, which aligns with discovering underlying biological principles. The general-purpose nature of Nvidia’s hardware and software libraries (CUDA, etc.) supports a wide range of physics-informed AI and other fundamental AI research that relies on high-performance computing.

Nvidia’s role is that of a meta-enabler. They provide the foundational “picks and shovels”—the GPUs, software libraries, and integrated platforms—that are essential for both quantum computing development and AI-driven scientific discovery. Their strategy to accelerate the entire ecosystem, rather than picking a single quantum hardware approach, positions them to benefit regardless of which specific qubit technologies ultimately prevail. For researchers pursuing first-fundamentals AI, Nvidia’s platforms are often the default choice for the intensive computations required, whether for training large neural networks, running complex simulations, or developing hybrid quantum algorithms. This makes Nvidia a critical infrastructure provider for the entire field discussed in this report.

C. Intel Corporation (INTC)

Intel is leveraging its deep expertise in silicon manufacturing to pursue a unique path in quantum computing, while also applying AI to enhance its core semiconductor business and explore related scientific domains.

1. Quantum Computing Strategy (Silicon Spin Qubits):

* What & How: Intel’s quantum computing strategy is centered on silicon spin qubits.90 In this approach, the quantum information (0/1) is encoded in the spin (up/down) of a single electron. These qubits are essentially single-electron transistors, allowing Intel to fabricate them using their advanced CMOS manufacturing processes, including 300-millimeter wafers and extreme ultraviolet (EUV) lithography.90 Intel believes silicon spin qubits offer significant advantages in scalability due to their extremely small size (potentially 1 million times smaller than other qubit types, measuring ~50 nm by 50 nm) and their compatibility with existing, highly refined semiconductor fabrication techniques.90 They have released research chips like “Tunnel Falls,” a 12-qubit silicon chip, and are making these available to the quantum research community through collaborations, such as with the Laboratory for Physical Sciences (LPS) at the University of Maryland, College Park’s Qubit Collaboratory (LQC), as part of the Qubits for Computing Foundry (QCF) program.90 This is part of Intel’s long-term strategy to build a full-stack commercial quantum computing system.

* Why: To leverage decades of world-leading transistor design and high-volume manufacturing expertise to address the critical challenges of qubit performance, yield, and scalability in quantum computing.90 The goal is to create a manufacturable and economically viable path towards large-scale, fault-tolerant quantum computers.

* Relevance to First Fundamentals AI: If Intel succeeds in producing large arrays of high-quality, stable silicon spin qubits, these could become another powerful hardware platform for running the quantum simulations necessary for first-principles AI. The precision and control offered by advanced semiconductor manufacturing could be key to achieving the qubit fidelities required for complex quantum algorithms relevant to scientific discovery (e.g., in chemistry or materials science).

2. AI for Scientific Discovery and Fundamental AI Research (Focus on Semiconductor Innovation & Computational Chemistry):

* What & How: The Intel AI Lab conducts both fundamental and applied research in Machine Learning. A key focus area is applying AI to improve chip design and computational chemistry.91 They develop machine learning and optimization techniques to solve complex, industrial-scale problems impacting semiconductor design and manufacturing. This research involves advanced ML architectures like Transformers, Graph Neural Networks (GNNs), and Diffusion models, as well as classical combinatorial optimization and search techniques.91 The aim is to publish findings, establish benchmarks, build open-source tools, and facilitate the transfer of these AI-driven technologies into commercial semiconductor design flows.

* Why: To harness AI to accelerate innovation, improve efficiency, and solve challenging problems within Intel’s core business of semiconductor design and manufacturing, and to explore related scientific areas like computational chemistry.91

* Relevance to First Fundamentals AI: Applying AI to optimize the highly complex physical and logical design processes of modern semiconductors—processes governed by the laws of physics and intricate engineering rules—has strong parallels with a “first fundamentals” approach. If AI can master the “fundamental truths” of chip design (e.g., using GNNs to understand circuit graphs or ML for optimizing layouts based on physical constraints), it demonstrates an ability to learn and apply deep, domain-specific knowledge. Their work in computational chemistry also directly aligns with using AI for molecular-level understanding.

Intel’s strategy exhibits a compelling synergy. They are using their cutting-edge AI research to enhance the design and fabrication of next-generation classical semiconductors and, potentially, their own silicon-based quantum chips. Concurrently, their quantum research aims to create a new type of computational hardware that could, in the long term, revolutionize fields like AI and scientific discovery, including areas Intel’s AI Lab is already exploring, such as computational chemistry. This creates a potential feedback loop: AI helps build better quantum chips, and quantum chips could eventually power more advanced AI. This internally focused technological advancement cycle, leveraging their unique manufacturing prowess, differentiates Intel’s approach from companies primarily focused on ecosystem enablement.

D. Broadcom Inc. (AVGO)

Broadcom’s activities in the quantum and advanced AI space are primarily focused on providing critical enabling technologies for security in the quantum era and fostering open AI ecosystems.

1. Quantum Computing Research (Quantum-Resistant Security):

* What & How: Broadcom is at the forefront of developing quantum-resistant network encryption solutions.92 They have introduced products like the Emulex Secure Fibre Channel Host Bus Adapters (HBAs), which encrypt data in-flight between servers and storage. These solutions are designed to comply with emerging post-quantum cryptography (PQC) standards and mandates, such as the United States’ Commercial National Security Algorithm (CNSA) 2.0 and the European Union’s NIS2 and DORA regulations.92 Their approach incorporates Zero Trust principles and silicon root-of-trust authentication.

* Why: To protect critical data and existing IT infrastructure from the future threat of quantum computers breaking current encryption algorithms, and to enable customers to meet new governmental cybersecurity mandates.92

* Relevance to First Fundamentals AI: While not directly enabling first-fundamentals AI through quantum computation, Broadcom’s work in PQC is a crucial “quantum deep tech” area. It ensures the security and integrity of the vast datasets and classical computational infrastructure that will be necessary for developing and deploying any advanced AI, including AI aimed at fundamental discovery. Secure data is a prerequisite for trustworthy AI.

2. AI for Scientific Discovery and Fundamental AI Research (Supporting Open AI Ecosystems):

* What & How: Broadcom is actively supporting the advancement of open AI ecosystems through strategic partnerships and investments. They have expanded their research sponsorship of UC Berkeley’s Sky Computing Lab with a significant financial donation to accelerate the lab’s mission.94 This collaboration focuses on projects aimed at creating interoperable and efficient AI infrastructure, such as:

* SAISI (Sky AI Stack Interoperability): A unified interoperability stack for AI.

* SkyPilot: For optimizing AI workloads across hybrid cloud environments.

* UCCL (Unified Collective Communication Library): For efficient communication between hardware accelerators in distributed AI training and inference.94

The emphasis is on supporting domain-specific expert models and efficient model post-training, which are becoming key for enterprise AI strategies. Broadcom engineers also contribute their expertise to these open-source efforts..9427

* Why: To foster innovation and ensure that the AI ecosystem develops in an open and interoperable manner, which can accelerate the adoption and impact of AI across industries. Supporting efficient infrastructure for training and deploying diverse AI models, including large and domain-specific ones, is critical.94

* Relevance to First Fundamentals AI: By contributing to open-source infrastructure and interoperability standards for AI, Broadcom helps create a more robust and accessible environment for all types of AI research, including fundamental AI. Efficient collective communication libraries (UCCL) and hybrid cloud solutions (SkyPilot) are essential for the large-scale training and deployment that advanced AI models, potentially those based on first principles, would require.

Broadcom’s strategy is thus twofold: securing today’s digital infrastructure against tomorrow’s quantum threats, and simultaneously helping to build the open, efficient, and interoperable AI infrastructure of the future. They are not directly developing quantum computers for simulation or pioneering new “first-fundamentals AI” algorithms themselves, but are providing essential security layers and contributing to the foundational ecosystem upon which such advanced AI will be built and operated.

E. Advanced Micro Devices, Inc. (AMD)

AMD plays a crucial role as an enabler in the quantum computing and AI for scientific discovery landscapes, primarily by providing high-performance classical computing hardware.

1. Quantum Computing Research (Enabling Hardware and Partnerships):

* What & How: AMD’s strategy is not to build quantum processors (QPUs) itself, but to support quantum computing efforts by supplying critical classical hardware components.95 This includes:

* AMD Instinct™ GPUs: Used in leading High-Performance Computing (HPC) centers (e.g., Oak Ridge National Labs’ Frontier supercomputer) to accelerate quantum research, enabling quantum simulations on classical hardware and the testing/validation of quantum algorithms before deployment on actual QPUs.95

* AMD FPGAs and Adaptive SoCs (e.g., Versal™ Prime, Zynq™ Ultrascale+™ RFSoCs): These are essential for the control and measurement units of various QPU modalities, ensuring reliable operation.95 Riverlane, for example, uses AMD Zynq SoCs in their quantum efforts.

AMD is involved in creating hybrid QC-HPC centers, such as with Europe’s LUMI supercomputer, bridging quantum and classical computing.95 They also partner with initiatives like Elevate Quantum to shape the future of quantum technology.95 AMD Research and Advanced Development (RAD) lists “quantum computing” as one of its “Future Directions”.96

* Why: To leverage their leadership in HPC to provide the necessary classical compute power that underpins quantum research and the operation of hybrid quantum-classical systems. They aim to enable scientific and engineering breakthroughs in the quantum domain by providing these essential tools.95

* Relevance to First Fundamentals AI: By accelerating quantum simulations on classical HPC systems and providing the control hardware for experimental quantum systems, AMD indirectly supports the development of first-fundamentals AI. These simulations and experiments are vital for understanding quantum systems well enough to model them from basic principles.

2. AI for Scientific Discovery and Fundamental AI Research (Responsible AI, Scientific Enablement):

* What & How: AMD is committed to advancing AI innovation responsibly and for societal benefit.97 Key initiatives include:

* AMD Artificial Intelligence and High-Performance Computing Fund: Provides researchers access to supercomputing power (over 20 petaflops) for advanced HPC and AI research across climate research, healthcare, life sciences, etc..97

* Deepspeed4Science Initiative: AMD is a founding partner (with Microsoft, national labs, universities) of this platform aimed at enabling large-scale scientific discovery using AI.97

* Powering Supercomputers for Science: AMD technology powers some of the world’s fastest supercomputers used for scientific research, like Lawrence Livermore National Laboratory’s El Capitan.97

* Research on AI Reasoning: AMD hardware (Instinct™ MI300X GPUs) is used by partners like Essential AI to conduct research into enabling reasoning capabilities in relatively small pretrained language models (e.g., OLMo-2-7B), challenging the notion that reasoning only emerges in very large models or during post-training.98

* Why: To empower scientific communities with the computational tools needed for breakthroughs, to foster responsible AI development, and to solve complex global challenges.97

* Relevance to First Fundamentals AI: AMD’s support for large-scale scientific AI projects and fundamental research into AI reasoning directly contributes to the ecosystem working towards first-fundamentals AI. Enabling more efficient reasoning in smaller models could make such advanced AI more accessible. Their hardware is critical for the computationally intensive tasks involved in physics-informed modeling, complex simulations, and training large foundation models that might underpin future AI for science.

AMD’s position is that of a critical enabler, providing the high-performance classical computing backbone essential for both the current stage of quantum research (simulations, QPU control) and the increasingly demanding needs of AI for scientific discovery. They are not developing quantum processors or specific “first-fundamentals AI” algorithms themselves, but their hardware is foundational to the efforts of many who are. Their support for initiatives like Deepspeed4Science and research into efficient AI reasoning demonstrates a commitment to advancing the scientific applications of AI.

F. ARM Holdings plc (ARM)

ARM Holdings is a key player in the semiconductor IP space, and its contributions to quantum computing and AI are focused on enabling efficient processing, particularly in specialized environments like cryogenic chambers for quantum computers and at the edge for AI.

1. Quantum Computing Research and Partnerships (Cryogenic Classical Control):

* What & How: ARM, in a significant collaboration with Equal1 (a quantum computing startup), has successfully tested its Cortex processor at cryogenic temperatures (3.3 Kelvin / -269.85°C).99 This breakthrough is crucial for integrating classical computing components (for control and readout) directly within the ultra-cold environment of a quantum computer’s cryo chamber. This co-location helps reduce thermal noise, which can cause errors in quantum computations, and helps preserve qubit coherence.99 This pragmatic approach leverages existing semiconductor expertise for quantum integration. Future plans include integrating more powerful processors like the Arm Cortex-A55 into Quantum System-on-Chips (QSoCs).99

* Why: To address a critical engineering challenge in scaling quantum computers: the interface between qubits and their classical control electronics. Placing control systems closer to the qubits in the same cryogenic environment can improve performance, reduce latency, and simplify the overall system architecture.99

* Relevance to First Fundamentals AI: Scalable and reliable quantum computers are a prerequisite for many envisioned applications of first-fundamentals AI, especially those requiring simulation of quantum systems. ARM’s work on cryogenic control electronics is a vital piece of “quantum deep tech” that contributes to making such quantum computers a reality.

2. AI for Scientific Discovery and Fundamental AI Research (Ubiquitous & Efficient AI Processing):

* What & How: ARM’s core AI strategy is to be the foundational architecture for “AI everywhere,” from cloud data centers to edge devices and endpoints, with a strong emphasis on power efficiency and scalability.100 Arm’s CPUs and GPUs have seen significant increases in AI processing capabilities. They provide the Arm NN software development kit and actively support open-source AI frameworks like TensorFlow and PyTorch to enable developers to optimize ML workloads on Arm-based hardware.101 The “Arm AI Readiness Index” is a report they produce to analyze global AI adoption and readiness.102 While their research directly targets enabling AI across many applications (e.g., automotive, IoT), specific large-scale projects focused on using AI to discover fundamental laws of science (like Google’s AlphaFold or Microsoft’s AQE) are not highlighted as primary ARM initiatives in the provided snippets. Their role is more as an enabler of the hardware platforms on which such AI can run efficiently.

* Why: To meet the insatiable demand for AI compute across a vast spectrum of devices and applications, ensuring that AI can be deployed efficiently and sustainably, particularly at the edge where power and thermal constraints are critical.101

* Relevance to First Fundamentals AI: While ARM may not be directly developing “first-fundamentals AI” algorithms for scientific discovery, their ubiquitous and power-efficient processor IP is crucial for deploying such advanced AI models once developed. Many complex AI systems, including those that might model CAS or perform reasoning from first principles, will need to run on diverse hardware, including edge devices for real-world interaction or data collection. ARM’s focus on efficiency is also critical, as advanced AI models are often computationally intensive.

ARM’s contribution to the quantum-AI nexus is thus twofold and highly enabling. Firstly, they are tackling a critical engineering bottleneck for scalable quantum computing through their development of cryogenic-capable classical processors for qubit control. This is a fundamental piece of the “quantum deep tech” puzzle. Secondly, their leadership in power-efficient processor design provides the hardware foundation for deploying AI broadly and sustainably, including any future “first-fundamentals AI” models that may emerge from the research efforts of others. They are providing key ingredients for both the quantum machines and the AI systems that will interact with them.

G. Samsung Group

Samsung, a global electronics and semiconductor conglomerate, is engaging with quantum technologies primarily through exploration and application of existing platforms, while its AI research is deeply integrated into its vast product ecosystem, with a growing interest in AI for scientific discovery, particularly in biology.

1. Quantum Computing Research (Exploration, e.g., Qiskit for Materials):

* What & How: Samsung’s direct quantum computing hardware development is not prominently featured in the provided snippets from 2000-present. However, there are indications of their exploration of quantum computing applications. Snippet 103, in a general discussion of Qiskit (IBM’s quantum SDK), mentions its use in material science and engineering for simulating electronic structures and designing new materials. While not explicitly stating Samsung’s direct use, as a major player in materials innovation (for displays, semiconductors, batteries), Samsung would logically explore such tools for R&D. Their broader engagement with advanced semiconductor technology is relevant to the physical realization of quantum devices.

* Why: To leverage potential breakthroughs in quantum simulation for advancing materials science, which is core to many of Samsung’s hardware businesses. Understanding and designing novel materials at a fundamental level can provide significant competitive advantages.

* Relevance to First Fundamentals AI: Using quantum simulations (e.g., via Qiskit or other platforms) for materials discovery is a direct application of quantum mechanics to understand matter from first principles. Insights gained could feed into AI models that learn these fundamental relationships, contributing to a “molecular perspective” on materials.

2. AI for Scientific Discovery and Fundamental AI Research (SAIT, Focus on Product Enhancement and Biology):

* What & How: Samsung Research, including the Samsung Advanced Institute of Technology (SAIT), conducts extensive AI research. Their primary focus is on device AI methodologies and applied AI techniques to enrich customer experiences across Samsung’s billions of devices.104 This includes:

* Language AI: Developing LLMs and code models, incorporating reinforcement learning, Retrieval-Augmented Generation (RAG), and prompt engineering. A key goal is to enhance reasoning capabilities by integrating user context and domain knowledge.104

* Voice and Sound AI: Aiming for human-like understanding of speech and sounds, including E2E Speech LLM solutions for voice recognition, synthesis, and sound-based biomarkers for health.104

* Vision AI: Providing comprehensive on-device vision solutions from sensor processing to high-level visual intelligence for smartphones and TVs, including text recognition and generative AI for image manipulation.104

While much of this is product-focused, there’s an increasing push towards more fundamental understanding. Samsung aims to tackle challenges like “integrating learning and perception with high-level knowledge” to improve reasoning.104

* Why: To create more intelligent, intuitive, and personalized user experiences on Samsung devices, and to improve internal productivity.104

* Relevance to First Fundamentals AI & Math Proof: Samsung’s goal to “enhance reasoning capabilities by incorporating the user’s context and domain knowledge” 104 points towards AI that can go beyond pattern matching to more structured understanding. While not explicitly “math proof AI” for discovering physical laws, building AI that can reason with domain knowledge is a step towards more fundamental AI. Their work on AI for understanding biological systems or materials (if pursued via quantum simulation) would also align with discovering underlying principles. The emphasis on “personalization” is strong, driven by observable user interactions with devices.

Samsung’s AI strategy appears heavily geared towards enhancing its extensive product ecosystem through sophisticated on-device and cloud-connected AI, focusing on language, voice, and vision modalities for improved user interaction and personalization. Their interest in quantum computing, as suggested by the relevance of platforms like Qiskit for materials science, likely stems from a long-term R&D perspective to drive innovation in the fundamental materials that underpin their hardware dominance. While “first fundamentals AI” for discovering objective laws of CAS is not their most prominent public message, their research into AI reasoning and potential use of quantum simulations for materials science could contribute to this area, particularly from a molecular and observable/personalized perspective.

H. LG Electronics Inc. (LG)

LG Electronics is strategically engaging with quantum computing through partnerships to explore applications, while its internal AI research is making targeted advances in scientific discovery, particularly in the biological domain.

1. Quantum Computing Research (Partnership for Application Exploration):

* What & How: LG Electronics joined the IBM Quantum Network in January 2022.105 This membership grants LG access to IBM’s quantum computing systems, quantum expertise, and the Qiskit open-source quantum information software development kit. LG’s stated aim is to explore applications of quantum computing in areas requiring the processing of large amounts of data, such as AI, connected cars, digital transformation, IoT, and robotics applications.105 They intend to leverage quantum hardware and software advances as they emerge, in line with IBM’s quantum roadmap, and provide workforce training to its employees in this domain.

* Why: To gain competency in quantum computing and identify potential breakthroughs that can be applied to LG’s future businesses, providing customers with new value.105 This is framed under an “open innovation strategy.”

* Relevance to First Fundamentals AI: By gaining access to quantum computing resources, LG can explore how quantum algorithms might solve complex problems currently intractable for classical computers. If applied to simulating physical or chemical systems, this could contribute to a first-principles understanding relevant to their diverse technology areas (e.g., materials for displays, batteries, or processes in smart appliances/robotics).

2. AI for Scientific Discovery and Fundamental AI Research (Focus on Biology/Drug Discovery):

* What & How: LG AI Research is actively involved in AI for scientific discovery, with a notable focus on biology and drug development. They are collaborating with Seoul National University to develop a “next-generation protein structure prediction AI” specifically targeting “multistate” protein structures—proteins that exist in various forms due to environmental and chemical changes.106 This research aims to go beyond current AI models that primarily predict single protein structures. The goal is to provide deeper insights into biological processes and accelerate drug development. This initiative is part of LG’s strategic focus on future growth drivers: AI, Bio, and Cleantech (ABC).106 LG AI Research also developed EXAONE, a 300 billion parameter multimodal (language and visual data) large-scale AI model in 2021 106, which likely underpins some of these advanced research efforts. They are also collaborating with The Jackson Laboratory (JAX) on predictive AI for Alzheimer’s and cancer.106

* Why: To achieve breakthroughs in understanding disease mechanisms and developing new treatments, aligning with LG’s strategic business interests in healthcare and biotechnology.106 The aim is to leverage the convergence of AI and biology.

* Relevance to First Fundamentals AI & Math Proof: The pursuit of predicting “multistate” protein structures is a significant step towards a more fundamental understanding of protein dynamics and function in complex biological environments. Proteins are key components of molecular CAS. Successfully modeling their multiple states and transitions based on underlying biophysical principles would be a major advance for “first fundamentals AI” in biology. While not explicitly “math proof,” such predictive models would need to be rigorously validated against experimental data and be consistent with known biophysical laws. This provides a molecular perspective on biological CAS.

LG’s strategy appears to be a pragmatic combination of leveraging established quantum ecosystems (like IBM’s) to explore broad applicability, while making focused, high-impact internal AI R&D investments in areas like computational biology that align with their declared future growth pillars (AI, Bio, Cleantech). Their work on multistate protein structure prediction is particularly relevant to the query’s interest in using AI to understand complex molecular systems from a fundamental perspective. This research aims to move beyond static snapshots to capture the dynamic nature of biomolecules, which is crucial for understanding their role in the adaptive processes of life.

I. NEC Corporation

NEC has a long history in quantum research and is currently focused on a specific type of quantum computer—annealing machines—for optimization problems, alongside broader AI research aimed at enhancing modeling and simulation.

1. Quantum Computing Research (Quantum Annealing):

* What & How: NEC’s quantum computing efforts, dating back to the 1990s (including early work on solid-state qubits), are now primarily centered on developing quantum annealing machines.107 These machines are specialized for solving combinatorial optimization problems. NEC aims to establish practical applications for all-to-all connected quantum annealing machines using superconducting parametron elements, targeting issues of coherence time and integration.107 They are working towards realizing a high-speed, high-accuracy optimization solution platform. While developing their own hardware, NEC also collaborates with other players; for instance, they became a global reseller of D-Wave’s Leap Quantum Cloud Service in 2021 and also offer simulated annealing services leveraging their SX-Aurora TSUBASA vector supercomputers.107

* Why: To provide solutions for complex optimization problems that are difficult for classical computers to solve efficiently due to massive computational requirements. Quantum annealers offer a different computational paradigm suited for finding optimal or near-optimal solutions in large search spaces.107 This has applications across various industrial domains.

* Relevance to First Fundamentals AI: Combinatorial optimization problems are prevalent in many scientific and engineering domains, including materials science (e.g., finding stable molecular configurations), logistics, and even in training some types of AI models. A powerful quantum annealer could thus be a tool that aids in parts of the first-fundamentals discovery process, particularly where optimization is a bottleneck. Understanding the optimal configurations or parameters of a CAS could be one application.

2. AI for Scientific Discovery and Fundamental AI Research (AI-Enhanced Modeling & Simulation):

* What & How: NEC Laboratories Europe conducts fundamental and applied research in AI and data science, with specific interests in computational biology, materials informatics, and human-centric AI.108 A key research direction is “machine learning for computation-intensive modelling and simulation tasks.” They aim to integrate machine learning to make simulation-based modeling more efficient and data-driven, while also focusing on developing trustworthy and explainable AI.108 Their research in natural language processing also contributes to AI’s ability to process and understand scientific literature.

* Why: To address societal challenges and improve industrial processes by developing cutting-edge AI that can handle complex data, enhance simulations, and provide understandable insights.108

* Relevance to First Fundamentals AI & Math Proof: NEC’s focus on using ML for “computation-intensive modelling and simulation tasks” and making these “more efficient and data-driven” directly aligns with the goals of AI for scientific discovery. If these simulations are physics-based or aim to model systems from underlying rules (as in computational biology or materials informatics), this contributes to a first-fundamentals approach. Developing explainable AI is also crucial for validating whether an AI has learned fundamental principles or merely correlations. While not explicitly “math proof AI” in the formal verification sense, AI that can improve and explain complex simulations is a step toward more rigorous computational science.

NEC’s strategy combines a specialized quantum approach (annealing for optimization) with a broader AI research agenda focused on enhancing complex modeling and simulation in scientific and industrial domains. This positions them to tackle optimization challenges within CAS and to develop AI tools that can more efficiently simulate and potentially discover the underlying dynamics of such systems. Their work in computational biology and materials informatics, powered by AI-enhanced simulation, points towards an interest in understanding systems from a molecular and material perspective.

J. Panasonic Corporation

Based on the provided information from 2000 to the present, Panasonic’s direct engagement in quantum deep tech for first-fundamentals AI appears limited or not extensively publicized, with their AI efforts being primarily application-driven for their existing product domains.

1. Quantum Computing Research:

* What & How: The provided snippets do not contain specific information about Panasonic’s direct research and development efforts in quantum computing hardware or software.109 Snippet 109, which mentions photonic quantum computing, refers to the company PsiQuantum, not Panasonic.

* Why: Not applicable based on available information.

* Relevance to First Fundamentals AI: Not directly applicable based on available information.

2. AI for Scientific Discovery and Fundamental AI Research:

* What & How: Panasonic’s AI research, as detailed on their “Panasonic AI” website, is focused on real-world applications that enhance living, business, and transportation.110 Their key AI research areas include image recognition, data analysis, robotics, voice/language processing, biometric data analysis, and AI/IoT platforms.110 The philosophy is centered on “making people’s life better” through these AI-driven innovations in their appliances and applications. There is no strong indication in these snippets of a major strategic push towards using AI for discovering fundamental laws of nature in physics, chemistry, or for building AI based on mathematical proof in the context of CAS or molecular objectivity.

* Why: To innovate and improve their existing product lines and business solutions, leveraging AI to create more intelligent and user-friendly devices and services.110

* Relevance to First Fundamentals AI: While Panasonic’s AI work involves sophisticated data analysis and pattern recognition, its primary aim appears to be applied AI for product enhancement rather than fundamental scientific discovery of objective laws governing CAS or matter from first principles. Their AI efforts are more aligned with improving observable perspectives and personalization within their product ecosystem.

Panasonic’s AI strategy, as presented in the available information, is largely centered on integrating AI capabilities into its diverse range of products and solutions to improve functionality and user experience. There is no significant evidence in the provided snippets to suggest a deep strategic investment in quantum computing or in fundamental AI research aimed at discovering objective, first-principles understanding of complex adaptive systems at the molecular or mathematical proof level. They appear to be more of a consumer and integrator of AI technologies for practical applications.

K. Toshiba Corporation

Toshiba has a well-established and focused presence in quantum technology, specifically in Quantum Key Distribution (QKD), while its AI research is increasingly targeting Embodied AI, which involves interaction with complex environments.

1. Quantum Computing Research (Primarily Quantum Key Distribution – QKD):

* What & How: Toshiba has a long and distinguished history in Quantum Key Distribution (QKD) research, initiated in 1999 at its Cambridge Research Laboratory (CRL).111 They have achieved several world-firsts in QKD, including demonstrations over 100 km of fiber (2003) and high continuous key rates (1 Mbit/s in 2010, 10 Mbit/s in 2017).111 Toshiba offers commercial QKD products with variants for multiplexed systems (operable on data-carrying fiber) and long-distance systems.111 Their vision is to secure global communications from threats posed by advances in computing, including future quantum computers, by applying the fundamental laws of Quantum Physics.111 They are also involved in QKD technology for satellite deployment and research into quantum random number generators, quantum light sources, and detectors.112 There is less emphasis in these snippets on general-purpose quantum computing hardware development.

* Why: To provide ultra-secure communication solutions for protecting sensitive data in an era where classical cryptography is increasingly vulnerable, especially with the advent of quantum computers.111

* Relevance to First Fundamentals AI: QKD is a critical “quantum deep tech” for ensuring the security of data and communications. While not directly a tool for AI to discover first principles, it provides the secure infrastructure necessary for any advanced computational system, including those that might handle sensitive scientific data or coordinate distributed AI/quantum research.

2. AI for Scientific Discovery and Fundamental AI Research (Embodied AI, Vision & Language):

* What & How: Toshiba’s Cambridge Research Laboratory (CRL) is significantly investing in and accelerating its research into Embodied AI.112 This involves creating agent-based AI that can physically manipulate objects, communicate with people, and assist in physical tasks. A key focus is on fast adaptation (enabling AI to adapt to new environments and tasks through interaction with humans and the environment) and continuous learning (generalizing knowledge into “common sense” from past experiences and multiple deployments).112 Their research addresses core challenges in Embodied AI, such as simplified interaction (e.g., pose estimation through natural language) and efficient training of world models (e.g., the ReCoRe framework).112 This leverages foundation models (large language and vision datasets) and incorporates expert knowledge for guided learning.

* Why: To develop versatile assistive AI systems that can revolutionize business operations across various sectors and adapt to the changing needs of industry by enabling intuitive deployment and collaborative human-machine task completion.112

* Relevance to First Fundamentals AI: Embodied AI, by its nature, must develop an understanding of the physical world and its dynamics to operate effectively. The development of “world models” and “common sense” reasoning within these agents, especially through interaction and adaptation in complex environments (which are CAS), aligns with the goal of AI understanding systems from an observable perspective. While not explicitly framed as discovering fundamental physical laws via mathematical proof, an AI that can build robust internal models of its environment to act intelligently is a step towards more fundamental understanding. The “fast adaptation” and “continuous learning” aspects are crucial for dealing with the non-stationarity of many CAS.

Toshiba’s strategy shows a strong focus on a mature area of quantum technology—QKD for security—and a forward-looking investment in Embodied AI. The Embodied AI research, with its emphasis on learning world models and common sense through interaction with complex, real-world environments, has the potential to contribute to AI systems that develop a more grounded and objective understanding of observable phenomena. This is distinct from, but complementary to, approaches that seek to derive understanding from purely mathematical or sub-molecular first principles. Toshiba’s AI aims to learn the “rules of the game” by playing it in the real world.

L. Hitachi, Ltd.

Hitachi is engaging with quantum technologies primarily through strategic investments, while its internal AI R&D is broad, with notable applications in data science for scientific and industrial modeling.

1. Quantum Computing Research (Investment and Exploration):

* What & How: Hitachi has launched a substantial venture capital fund (HV Fund IV, $400 million), managed by Hitachi Ventures, which targets investments in disruptive technology startups, including those in quantum computing, AI, nuclear fusion, and bioengineering.113 This fund is part of a larger $1 billion CVC investment strategy. This indicates a strategic interest in accessing and leveraging external innovations in the quantum space rather than a primary focus on in-house quantum hardware development, based on the provided snippets.

* Why: To identify and support emerging technologies and startups that can create or disrupt major markets, particularly in strategic digital and industrial sectors, and to anticipate future technological trends beyond Generative AI that are relevant to Hitachi’s Social Innovation Business.113

* Relevance to First Fundamentals AI: By investing in quantum computing startups, Hitachi is positioning itself to benefit from future quantum capabilities that could enable first-principles simulations and other computationally intensive tasks relevant to scientific discovery and advanced AI.

2. AI for Scientific Discovery and Fundamental AI Research (Data Science for Modeling & Simulation):

* What & How: Hitachi is actively involved in R&D utilizing data science and AI to solve social issues and innovate businesses.114 Their research areas include:

* Data Science Research: Machine learning, reinforcement learning, deep learning, mathematical optimization, modeling and simulators, knowledge graphs. Application examples include people flow and logistics optimization, anomaly detection, digital twins, machine learning integration and application to natural sciences, and knowledge management.114

* Intelligent Vision Research: Multimodal AI with image analysis, XR/metaverse, generative AI for applications in railways, manufacturing, public safety, and medical care.114

* Media Intelligence Processing Research: Generative AI (domain-specific LLMs, LLM agents, multimodal AI), NLP, and speech recognition, with an emphasis on transparency and fairness.114

* Why: To drive digital transformation for customers, create solutions for prediction, judgment, and optimization, and contribute to social innovation through the co-evolution of AI and humans.114

* Relevance to First Fundamentals AI & Math Proof: Hitachi’s research in “machine learning integration and application to natural sciences,” “modeling and simulators,” and “digital twins” 114 is highly relevant to the pursuit of first-fundamentals AI. Digital twins, for instance, aim to create high-fidelity virtual replicas of physical systems, which often requires modeling based on underlying physical laws. The use of knowledge graphs can provide a structured, symbolic reasoning layer to AI systems, potentially making their understanding more robust and explainable. Mathematical optimization is also a core component of many scientific discovery processes. While “math proof” in a formal sense isn’t explicitly stated as an AI output, the rigor required for effective modeling and simulation in natural sciences moves AI in that direction.

Hitachi’s approach appears to be a blend of strategic external investment in disruptive areas like quantum computing, coupled with strong internal AI and data science capabilities focused on solving complex real-world problems, including those in the natural sciences and industrial systems. Their work on AI for modeling, simulation, and digital twins, especially when applied to natural sciences, has the potential to develop AI systems that gain a deeper, more mechanistic understanding of complex adaptive systems. The integration of knowledge graphs could further enhance the ability of these AI systems to reason from a more structured and potentially objective knowledge base.

M. NTT Group (Nippon Telegraph and Telephone)

NTT is pursuing a distinctive dual strategy, investing in both novel quantum computing hardware (including optical approaches) and fundamental research into the “physics” of AI itself, particularly how advanced models like LLMs learn and reason.

1. Quantum Computing Research (Optical & Superconducting Systems, LASOLV):

* What & How: NTT Computer and Data Science Laboratories are engaged in theoretical research on quantum system architectures and the development of system/software technology for practical quantum computer applications.115 They are exploring multiple hardware modalities, including optical quantum systems and superconducting systems. A notable development is their Ising machine called LASOLV™, which executes operations using light, designed for solving optimization problems.115 NTT is also researching error correction and suppression techniques. Their overarching vision includes the Innovative Optical and Wireless Network (IOWN), a next-generation infrastructure that could integrate quantum technologies for new services like Digital Twin Computing (DTC) for future prediction by combining real and digital worlds.115

* Why: To overcome the limits of current ICT, enable high-speed solutions for problems intractable for classical computers (e.g., optimization, chemical simulations), and build new information processing infrastructures.115

* Relevance to First Fundamentals AI: NTT’s development of specialized quantum hardware like LASOLV™ (an Ising machine) directly addresses complex optimization problems, which are often components of modeling CAS or scientific discovery workflows. More broadly, their research into general-purpose quantum computing and error correction contributes to the hardware foundation needed for future quantum simulations from first principles. The IOWN concept, if it successfully integrates quantum resources, could provide the networked infrastructure for large-scale quantum-enhanced AI.

2. AI for Scientific Discovery and Fundamental AI Research (Physics of AI, LLM Understanding):

* What & How: NTT Research has established a Physics of Artificial Intelligence (PAI) Group (April 2025) dedicated to deepening the understanding of AI mechanisms, observing AI learning/prediction behaviors, and fostering trust between AI and humans.116 Their research, presented at conferences like ICLR, explores fundamental aspects of AI:

* Uncertainty in LLMs: Investigating “forking tokens” in neural text generation, suggesting LLMs can be highly sensitive to small changes in intermediate steps, impacting output predictability.116

* In-Context Learning (ICL): Analyzing how LLMs adapt pre-trained semantics to adopt context-specific ones, viewing ICL as a mixture of algorithms rather than a monolithic capability.116

* Concept Learning and Compositional Generalization: Using structured identity mapping (SIM) tasks to study how models learn concepts and generalize compositionally.116

* Emergence in Neural Networks: Proposing phenomenological models (inspired by percolation theory) to understand the causal factors behind emergent properties in transformers trained on formal languages.116

The PAI Group aims to integrate ethics from within AI mechanisms and create controllable experimental spaces for AI, borrowing from experimental physics methodologies.116

* Why: To move beyond black-box AI by understanding how AI models like LLMs fundamentally learn, reason, and generalize. This is crucial for building more reliable, trustworthy, sustainable, and socially resilient AI systems.116

* Relevance to First Fundamentals AI & Math Proof: NTT’s “Physics of AI” research is highly pertinent to the user’s query. By seeking to uncover the underlying principles and causal factors of AI behavior (e.g., emergence, in-context learning), they are essentially trying to establish a “first-principles” understanding of AI itself. This is a meta-level approach: before AI can discover fundamental truths about external CAS, we need to understand the fundamental truths of AI. Research into compositional generalization and the formal language capabilities of transformers also touches upon aspects related to structured reasoning and potentially mathematical interpretability of AI models.

NTT’s strategy is particularly forward-looking. They are not only developing novel quantum hardware (like optical quantum computers) but are also deeply investigating the fundamental workings of current advanced AI models. This dual approach – building new computational tools and simultaneously deconstructing how current AI learns and reasons – positions them uniquely. An AI that understands its own learning processes and limitations from a “physics” perspective would be a significant step towards an AI that can more objectively understand the physics and fundamental rules of other complex systems. The IOWN initiative also points to a future where quantum and classical resources are seamlessly integrated within a high-performance network, enabling new forms of computation and AI-driven services like Digital Twin Computing.

V. Chinese Competitors: National Strategy and Key Players

China has made the development of quantum technologies and artificial intelligence a cornerstone of its national strategy, aiming for global leadership in these transformative fields. This ambition is backed by significant state funding, coordinated research initiatives, and a rapidly evolving ecosystem of academic institutions and commercial enterprises.83

A. Overview of China’s National Quantum and AI Strategy

China’s national strategy emphasizes a centralized, state-guided approach to fostering breakthroughs in quantum information science (QIS) – encompassing quantum computing, quantum communication, and quantum sensing – and in all facets of AI, from fundamental research to widespread application.

• Quantum Ambitions: China has achieved notable successes, particularly in quantum communication, with the Micius satellite enabling intercontinental quantum key distribution (QKD) and the establishment of extensive terrestrial QKD networks like the Beijing-Shanghai link.117 In quantum computing, significant progress has been made with photonic systems like Jiuzhang (demonstrating quantum computational advantage on specific boson sampling tasks) and superconducting processors like Zuchongzhi.117 The government has invested heavily, with figures cited upwards of $15 billion for quantum R&D, and established key research hubs like the University of Science and Technology of China (USTC) in Hefei and the Beijing Academy of Quantum Information Sciences (BAQIS).117 The strategy involves fostering domestic talent and technology, with an increasing emphasis on state control and coordination of research efforts.117
• AI Prowess: China is a global leader in AI research output and deployment.118 The government has prioritized AI development, leading to a vibrant ecosystem of startups and established tech companies working on Large Language Models (LLMs), computer vision, autonomous driving, and other AI applications.119 Initiatives often involve public-private partnerships and substantial government support for designated “national high-tech enterprises”.119 There’s a strong push for AI to drive economic growth and enhance national capabilities. Chinese scientists have rapidly incorporated AI into their scientific work, outpacing EU and US counterparts in the sheer output of AI-driven research papers, which also show high novelty and impact.118

B. Key Chinese Corporate Players and Research Institutions

Several Chinese companies and research institutions are at the forefront of quantum and AI development.

1. Baidu (BIDU):

* Quantum Program (Shift in Strategy): Baidu established its Institute for Quantum Computing in 2018, with research pillars in Quantum AI, Quantum Algorithms, and Quantum Architecture.134 They developed the 10-qubit superconducting quantum computer “Qian Shi” and the integrated software-hardware solution “Liang Xi”.136 Their Paddle Quantum toolkit was designed for quantum machine learning, integrating with the broader PaddlePaddle deep learning platform.136

However, in early 2024, Baidu announced its intention to donate its quantum computing laboratory and equipment to the state-backed Beijing Academy of Quantum Information Sciences (BAQIS).121 This move, following a similar decision by Alibaba, suggests a consolidation of quantum hardware efforts under more direct government oversight. Baidu’s 2023 20-F filing (made in March 2024) mentioned past investment in quantum computing as part of its Baidu Brain AI technology engine 147, but the 2024 20-F (filed March 2025) 148, while detailing significant R&D in AI (RMB 22.1 billion in 2024), does not provide explicit details on the current status or future of its in-house quantum computing hardware research after the BAQIS donation. The focus in the latest annual report is heavily on generative AI and foundation models.148 It’s plausible Baidu will continue quantum software and algorithm research, leveraging platforms like Paddle Quantum, and collaborate with BAQIS or other national labs for hardware access. The shift implies that for capital-intensive quantum hardware development, China might be centralizing efforts in state-led institutions, while tech companies focus on AI and quantum software/applications.

* AI (ERNIE, PaddlePaddle, AI for Science):

* ERNIE LLM: Baidu’s “Enhanced Representation through kNowledge Integration” (ERNIE) series of LLMs (e.g., ERNIE 3.0 Titan, ERNIE 3.5, ERNIE 4.5, ERNIE X1) are powerful foundation models with capabilities in language understanding, generation, reasoning, and multimodal processing.138 A key feature of ERNIE is its integration with large-scale knowledge graphs to enhance factual accuracy and reasoning capabilities.138 This neuro-symbolic approach aims to make the models more knowledgeable and less prone to hallucination. ERNIE X1 is specifically highlighted for “deep-think reasoning,” breaking down complex problems into logical steps.150

* PaddlePaddle Platform: This is Baidu’s open-source deep learning platform, which has gained widespread adoption in China.138 It supports a comprehensive ecosystem for AI development, including:

* PaddleHelix: A biocomputing platform for AI-driven drug discovery, mRNA vaccine design, compound representation learning (HelixGEM), and protein structure prediction.138 This directly relates to AI for scientific discovery at a molecular level.

* Paddle Quantum: As mentioned, for quantum machine learning.143

* Support for various AI tasks, including computer vision (PaddleDetection, PaddleSeg), NLP, and recommendation systems.155

* Fundamental AI Research (Physics-Informed, Neuro-Symbolic, Causal, CAS):

* Physics-Informed AI: PaddlePaddle is used for applications involving physics-informed neural networks (PINNs), for instance, in simulating information propagation dynamics which can be described by differential equations 159, and for solving advection-diffusion-reaction equations.160

* Neuro-Symbolic AI: The ERNIE architecture’s integration of knowledge graphs for reasoning is a clear example of a neuro-symbolic approach.138 Baidu has also published early research on agents learning language in virtual worlds by disentangling language grounding from other computational routines, hinting at symbolic concept formation.161

* Causal AI: Baidu Research has published on mitigating confounding and selection biases in personalized recommendation using causal approaches.162 While not as extensive as dedicated causal AI suites from some Western counterparts in the provided snippets, it shows engagement with causal principles.

* AI for Modeling Complex Adaptive Systems (CAS): Baidu’s work on urban computing (e.g., segmenting urban areas with road networks 162), modeling crowd flow 162, and autonomous driving inherently deals with CAS. Their intelligent edge platform (BIE) is designed for scenarios like edge machine vision and edge data analysis in complex environments.163 Research on “Inner Thinking Transformer” (ITT) explores dynamic allocation of computation for critical tokens, allowing deeper processing for complex inputs, which is relevant for systems adapting to varying complexity.164 Their investigation into logging policy confounding in ranking models for Baidu-ULTR dataset also touches on understanding complex system interactions.165 System dynamics modeling has been applied to analyze Baidu Encyclopedia as a peer production system, a form of CAS.166

* Personalization AI & Molecular Modeling: ERNIE and PaddlePaddle are used to power personalized search and recommendation in Baidu’s core internet services. PaddleHelix directly addresses molecular modeling for drug discovery and biological understanding.138

* Quantum Sensing: The Baidu Research page on Quantum Computing mentions “Quantum AI” with key areas including Machine Learning, Information Security, and Blockchain, and “Quantum Algorithm” including Quantum Simulation and Quantum Search.134 While “quantum sensing” is not explicitly listed as a primary research topic there, the foundational work in quantum algorithms and simulation could be applicable. The broader Chinese national strategy does include quantum sensing.120 No specific Baidu quantum sensing projects were detailed in the provided snippets for 2000-present.138

* Pursuit of Objectivity/Fundamental Understanding: ERNIE’s knowledge graph integration and reasoning capabilities 150 represent an effort to build AI that can reason with structured, factual knowledge, moving beyond purely statistical pattern matching. This aligns with building more objective AI. PaddleHelix’s application to drug discovery and mRNA vaccine design based on molecular properties 138 aims for a fundamental understanding of biological systems. The use of PINNs within PaddlePaddle 159 directly incorporates physical laws. Baidu’s AI ethics principles emphasize safety, controllability, and technology serving humanity 168, which requires a degree of objective and reliable AI.

2. Origin Quantum (Hefei Benyuan Quantum Computing Technology Co., Ltd.):

* What & How: Origin Quantum is a full-stack quantum computing company spun out of the Chinese Academy of Sciences’ Key Laboratory of Quantum Information.169 They develop quantum chips (superconducting, e.g., “Wukong” with 72 computational qubits and 126 couplers 170), quantum measurement and control systems, quantum computer operating systems, and a quantum computing cloud platform (OriginQ Cloud 169). Their software ecosystem includes QPanda, VQNet, and pyqpanda-algorithm.172 They are actively promoting the industrialization of quantum computing in China and have exported a superconducting quantum chip.117

A significant recent development (April 2025) is the use of their Origin Wukong quantum computer to fine-tune a billion-parameter AI (LLM) model, a claimed world-first.171 This involved converting model weights into a hybrid of quantum neural networks (QNNs) and classical tensor networks, reportedly improving training effectiveness by 8.4% and reducing parameters by 76%.171 This “Quantum Weighted Tensor Hybrid Network” (QWTHN) or “Quantum Tensor Hybrid Adaptation” (QTHA) leverages quantum state superposition to overcome classical rank limitations in LoRA-like fine-tuning.179

* Why: To become a leader in China’s quantum computing industry, providing full-stack solutions from hardware to applications, and to explore the synergy between quantum computing and AI.169 The LLM fine-tuning work aims to address the “computing power anxiety” associated with large AI models.171

* Relevance to First Fundamentals AI & Math Proof:

* The development of QNNs and their application to LLM fine-tuning 171 is a direct intersection of quantum deep tech and advanced AI. If quantum approaches can fundamentally improve the efficiency and expressiveness of AI models, this could accelerate the development of AI capable of tackling more complex, fundamental problems.

* The use of quantum superposition and entanglement in QWTHN to enhance model adaptation 180 suggests leveraging quantum mechanics for more powerful information processing, a step beyond classical AI.

* While not explicitly “math proof AI” in the snippets, the engineering-ready foundation for “quantum-enhanced AGI systems” 179 points to ambitions for highly capable AI.

* Other Research Areas (Physics-Informed, Neuro-Symbolic, Causal, CAS, Sensing):

* Physics-Informed AI: Recent preprints describe Quantum Physics-Informed Neural Networks (QPINNs) integrating quantum computing and ML to solve or discover differential equations, including PDEs on continuous-variable quantum computers.182 This is a direct approach to “first fundamentals” modeling.

* Neuro-Symbolic AI: While not explicitly detailed for Origin Quantum in these snippets, the broader Chinese AI ecosystem (e.g., Baidu’s ERNIE) uses neuro-symbolic approaches. The general field is very active.13

* Causal AI: General context on Causal AI exists 185, but specific Origin Quantum projects are not detailed in the snippets.

* Complex Adaptive Systems AI: Quantum annealing (which Origin Quantum may explore given China’s interest) is suited for optimization in CAS. General research links quantum theory to CAS.188

* Quantum Sensing: While Origin Quantum’s primary focus in these snippets is quantum computing, quantum sensing is a major pillar of China’s national quantum strategy 120 and a field with active global research.192 Specific Origin Quantum sensing projects are not detailed here.

* White Papers/Roadmap: Origin Quantum (Quantinuum, a different company, is often mistaken here due to similar naming of “Quantum Origin” product 137) has a cloud platform and lists application areas like FinTech, Biochemistry, AI & Big Data.172 The paper on QWTHN/QTHA 179 acts as a form of research publication detailing their technology. Broader quantum roadmaps exist from national bodies.192

3. Other Chinese Entities (e.g., Alibaba, Tencent, Huawei, QuantumCTek, Startups):

* Alibaba: Like Baidu, Alibaba had a quantum computing research lab (part of DAMO Academy) but announced its closure in late 2023, donating the lab and equipment to Zhejiang University.117 Their focus has reportedly shifted more towards AI R&D and its applications.128 Their AI models (e.g., Qwen) are used by many enterprises.132

* Tencent: Invests in AI research and applications, similar to Alibaba and Baidu, and is a major player in China’s AI ecosystem.126 Specific deep quantum hardware efforts are less publicized in these snippets compared to their AI work.

* Huawei: A major telecommunications and technology company, Huawei invests heavily in AI R&D across various domains, including AI for its cloud services and telecommunications infrastructure.126 They also have research teams developing quantum algorithms and prototype hardware.117

* QuantumCTek: A prominent Chinese company specializing in quantum communication and quantum cryptography products and services.117 They are a key player in building China’s QKD infrastructure.

* SpinQ: Provides superconducting and NMR quantum computers, a quantum computing cloud platform, and educational products. They have exported a superconducting quantum chip.178

* Other Startups and Research Institutions: Hefei’s “Quantum Avenue” hosts numerous quantum startups.117 USTC and other CAS institutes are central to fundamental quantum research.117 BAQIS is a key government-backed institute now integrating labs from former corporate efforts.117 Companies like DeepSeek (AI LLMs) 119, Zhipu AI, MiniMax, Baichuan AI are significant in the generative AI space.132 CIQTEK offers quantum sensors and computers.209

The strategy of Chinese tech giants like Baidu and Alibaba appears to be shifting towards a model where fundamental, capital-intensive quantum hardware research is increasingly led by state-backed academic and research institutions (like BAQIS and universities). The companies then focus on AI development (LLMs, industry applications) and potentially quantum software, algorithms, and cloud platforms, collaborating with these national labs for hardware access. This allows them to leverage national investments while concentrating their resources on areas closer to commercial application. Companies like Origin Quantum represent a more specialized, full-stack quantum approach emerging from this academic-industrial complex. The overarching goal is to create a robust national ecosystem that can compete globally in both quantum and AI, with a strong emphasis on practical applications and national strategic priorities. The development of QNNs for LLM fine-tuning by Origin Quantum 171 is a prime example of this drive to find practical quantum advantages in synergy with leading-edge AI.

VI. Thematic Analysis: Quantum Deep Tech and First-Fundamentals AI

The pursuit of “quantum deep tech” and “first-fundamentals AI” is not merely an academic exercise; it’s a multi-faceted endeavor with profound implications for how complex adaptive systems (CAS) are understood and interacted with. This section delves into specific technological themes—physics-informed AI, neuro-symbolic AI, causal AI, AI for CAS modeling, quantum-enabled scientific discovery, and the role of quantum sensing—highlighting how they contribute to the overarching goal of achieving objective, principle-based understanding.

A. Physics-Informed Artificial Intelligence (PINN)

Physics-Informed Neural Networks (PINNs) and related physics-informed machine learning (PIML) approaches represent a significant step towards building AI models that are grounded in the fundamental laws governing physical systems.11

• What & How: PINNs integrate governing physical laws, typically expressed as partial differential equations (PDEs) or ordinary differential equations (ODEs), directly into the training process of neural networks.11 This is often achieved by adding a term to the loss function that penalizes deviations from these physical laws, effectively biasing the neural network to learn solutions that are physically consistent.11 Automatic differentiation is commonly used to compute the PDE residuals within the network.160 Research focuses on adapting activation functions, refining gradient optimization, innovating network structures, and enhancing loss function architectures to improve PINN performance.36
• Why: The primary motivations for using PINNs are to:

• Improve generalization with limited data: By incorporating known physics, PINNs can often learn accurate solutions even when observational data is scarce or expensive to obtain, a common challenge in scientific machine learning.11
• Enhance model interpretability: Solutions that adhere to physical laws are inherently more interpretable and trustworthy.11
• Solve complex differential equations: PINNs offer a mesh-free approach to solving PDEs, which can be advantageous for complex geometries or high-dimensional problems.160
• Enable physics discovery: In some cases, PINNs can be used to infer unknown physical parameters or even discover governing equations from data.11

• Applications & Corporate Engagement:

• Google: Research on physics-aware downsampling for scalable flood modeling, where deep learning optimizes terrain maps so that coarse-grid flood predictions match fine-grid, physics-based simulations.28 They also fund academic research using PINNs for wildfire pollutant transport.31
• Microsoft: Applying physics-informed AI for simulating materials and plasma behavior in fusion energy research.67
• Baidu: Their PaddlePaddle platform supports PINN applications, for example, in modeling information propagation dynamics (which can be described by ODEs/PDEs) 159 and solving advection-diffusion-Langmuir adsorption processes.160
• Nvidia: Their accelerated computing hardware is crucial for training PINNs and other physics-based simulations.
• General: PINNs are applied across fluid dynamics, mechanics, materials science, electromagnetism, biomedical engineering, quantum mechanics, and renewable energy systems.10 Quantum Physics-Informed Neural Networks (QPINNs) are an emerging extension, integrating quantum computing to solve or discover differential equations.182

• Relevance to First Fundamentals AI & CAS Objectivity: PINNs directly embody the idea of “first-fundamentals AI” by embedding mathematical formulations of physical laws (first principles) into the AI learning process. This forces the AI to discover solutions that are not just correlational but are consistent with established scientific understanding. For CAS that are governed by known physical laws (e.g., fluid dynamics in weather systems, plasma physics in fusion reactors), PINNs can help create more objective and accurate models by ensuring adherence to these fundamentals. This moves beyond simple observation (like PV=nRT) to models that understand the underlying differential equations.

B. Neuro-Symbolic Artificial Intelligence (NSAI)

Neuro-Symbolic AI (NSAI) seeks to combine the strengths of connectionist deep learning (neural networks for pattern recognition and learning from large, unstructured data) with symbolic AI (logic, rules, knowledge representation for reasoning and interpretability).12

• What & How: NSAI architectures integrate neural and symbolic components in various ways. Kautz’s taxonomy includes approaches like: Symbolic[Neural] (symbolic methods invoking neural ones, e.g., AlphaGo), Neural|Symbolic (neural perception feeding symbolic reasoning), Neural: Symbolic → Neural (symbolic reasoning generating/labeling data for neural training), and Neural (neural models calling symbolic engines).13 The goal is to create systems that can learn from experience (neural) and reason from acquired knowledge (symbolic).183 This can involve using knowledge graphs, logical rules, and semantic representations within or alongside neural networks.12
• Why:

• Enhanced Reasoning & Generalization: To overcome the limitations of purely neural models in complex reasoning, common-sense understanding, and out-of-distribution generalization.12
• Improved Interpretability & Explainability: Symbolic components provide a more transparent and understandable reasoning process compared to “black-box” neural networks, which is crucial for trustworthy AI in critical applications.12
• Data Efficiency: Leveraging explicit knowledge can reduce the reliance on massive datasets for training.12
• Handling Abstract Knowledge: Symbolic manipulation is key for representing and reasoning about abstract concepts.13

• Applications & Corporate Engagement:

• Google: AlphaGeometry, for solving Olympiad-level geometry problems, is a prime example of a neuro-symbolic system, combining a neural language model for proposing constructions with a symbolic deduction engine for formal proof.30 Earlier work includes Neural Symbolic Machines for semantic parsing 38 and research on adaptable neural memory over symbolic knowledge bases.39
• Microsoft: Research on neuro-symbolic computation for inference reasoning in NLP and multimodality 68, and neuro-symbolic visual reasoning.69
• Baidu: Their ERNIE LLMs integrate knowledge graphs to enhance reasoning and factual grounding, a neuro-symbolic approach.138 Early research explored agents learning language via disentangled grounding.161
• General: Applications span robotics (complex task execution), NLP (logical consistency), autonomous systems, healthcare (diagnosis with medical knowledge integration), and scientific discovery (hypothesis generation and testing).12

• Relevance to First Fundamentals AI & CAS Objectivity: NSAI is highly relevant to achieving first-fundamentals AI. By incorporating symbolic reasoning based on logic and explicit knowledge (which can represent fundamental truths or axioms), NSAI aims to build models that can perform deductive and abductive reasoning, akin to mathematical proof or scientific derivation. This allows AI to move beyond pattern recognition to more structured, verifiable understanding. For CAS, NSAI could allow modeling not just with learned patterns but also with explicit rules representing known agent behaviors or system constraints, leading to more objective and interpretable models of system dynamics. The “System 1” (intuitive, neural) and “System 2” (deliberative, symbolic) cognitive framework often invoked for NSAI 13 mirrors the human ability to combine rapid pattern matching with slower, rigorous reasoning—a capability essential for deep scientific understanding.

C. Causal Artificial Intelligence (Causal AI)

Causal AI focuses on understanding and modeling true cause-and-effect relationships, moving beyond the identification of mere correlations, which is a fundamental limitation of many traditional machine learning systems.14

• What & How: Causal AI employs techniques from causal inference to determine how changes in one variable directly influence another.14 This involves:

• Causal Discovery: Identifying causal relationships from data, often using graphical models like Directed Acyclic Graphs (DAGs) or Structural Causal Models (SCMs) to represent assumptions about causality.14
• Causal Estimation: Quantifying the strength of causal effects, for example, the average treatment effect (ATE) of an intervention.
• Counterfactual Reasoning: Simulating alternative scenarios (“what if?”) to assess the impact of different actions or conditions.15 Key steps include data checking (quality, confounders), model selection (DAGs/SCMs, identifying mediators/colliders), and identification (determining if the causal effect can be estimated from observed data).14

• Why:

• Improved Reliability & Robustness: Models based on causal relationships are expected to be more robust to changes in data distribution and less prone to spurious correlations.14
• Enhanced Explainability & Transparency: Understanding causal mechanisms provides clearer explanations for AI predictions and decisions, moving away from “black-box” models.14
• Actionable Insights & Better Decision-Making: Causal knowledge allows for more effective interventions and policy-making, as it predicts the actual outcomes of actions.15
• Avoiding Bias: Causal inference helps identify and mitigate biases that can arise from confounding variables.14

• Applications & Corporate Engagement:

• Google: Research on causal inference methods for combining RCTs and observational data 44, and on robust agents learning causal world models.46 Applications in online advertising and healthcare to improve understanding and outcomes.47 Causaly, a company using AI for biomedical knowledge discovery, leverages Google Cloud.217
• Microsoft: Significant investment in a Causal AI Suite (DoWhy, EconML, Causica, ShowWhy) to democratize causal inference and foster use-inspired basic research.71
• Baidu: Research on mitigating biases in recommender systems using causal approaches.162
• General: Applications span healthcare (treatment effects, disease pathways), finance (risk analysis), marketing (attribution modeling, ROI), manufacturing (root cause analysis), and policy evaluation.15 The global Causal AI market is projected for significant growth.218

• Relevance to First Fundamentals AI & CAS Objectivity: Causal AI is absolutely central to the idea of discovering “objectivity” in CAS and building “first-fundamentals AI.” Understanding the true causal web of interactions within a complex system, rather than just observing correlations, is the definition of understanding its underlying mechanisms. A first-principles AI must be able to reason causally to explain why a system behaves the way it does, not just predict what it will do based on past patterns. For example, PV=nRT is an observational law; a causal AI would seek to understand the kinetic theory of gases that causes this relationship. Causal models provide the structure for such deeper understanding, moving AI from simple pattern recognition to genuine explanatory power regarding the omnipresence and interactions of matter and molecular systems.

D. AI for Modeling Complex Adaptive Systems (CAS)

Modeling CAS effectively requires AI approaches that can capture non-linearity, emergence, agent interactions, and feedback loops. This often involves moving beyond traditional supervised learning.

• What & How: AI techniques for CAS modeling include:

• Agent-Based Modeling (ABM) enhanced by AI: AI can be used to define more sophisticated agent behaviors or to calibrate/validate ABMs.
• Reinforcement Learning (RL): Agents learn optimal policies through interaction with a (potentially simulated) complex environment.4 Model-based RL, where an AI learns a model of the system’s dynamics, is particularly relevant.4
• Generative Models & LLMs: Can be used to simulate CAS dynamics, generate synthetic data for training other models, or even act as components (agents) within a CAS simulation.5
• Graph Neural Networks (GNNs): Suitable for representing and reasoning about interactions in networked systems, a common feature of CAS.211
• System Dynamics (SD) combined with AI: AI can help build or analyze SD models, which explicitly map feedback loops and accumulations.49
• Multi-Agent Systems (MAS): AI research is increasingly focusing on MAS where multiple autonomous agents collaborate or compete, exhibiting complex emergent behaviors.51

• Why: To understand, predict, and potentially influence the behavior of real-world CAS in fields like economics, social science, ecology, epidemiology, and urban planning. This is crucial for addressing complex societal challenges.5
• Applications & Corporate Engagement:

• Google: Research on CAS for societal context in ML 49, the SYMBIOSIS platform for systems thinking 52, modeling weather/climate 25, and emergent behavior in multi-agent game environments.51
• Microsoft: Research on self-adapting AI agents (Trace framework) 72, multi-agent systems 73, AI for fusion plasma modeling (a CAS) 67, and “Societal AI” frameworks.73
• Baidu: Work on urban computing (crowd flow, road networks) 162, autonomous driving, and the Inner Thinking Transformer for adaptive computation.164 System dynamics applied to Baidu Encyclopedia.166
• General: AI governance itself is being viewed through a CAS lens.5 The study of AI model collapse (when AI trained on AI-generated data degenerates) is also a CAS phenomenon.5

• Relevance to First Fundamentals AI & CAS Objectivity: To achieve an objective understanding of a CAS, AI must capture its fundamental generative mechanisms—how micro-level agent interactions and rules lead to macro-level emergent behavior. First-fundamentals AI for CAS would involve learning these generative rules, possibly through a combination of causal discovery, reinforcement learning on accurate system models, and neuro-symbolic representation of agent logic and interactions. The goal is to create AI that doesn’t just fit data from a CAS but understands its “physics” or “social physics.” This allows for true prediction of novel states and the effects of interventions, leading to objective insights rather than superficial pattern matching.

E. Quantum-Enabled Scientific Discovery (Materials, Chemistry, Biology, Physics)

Quantum computers promise to revolutionize scientific discovery by enabling simulations of quantum systems that are intractable for even the most powerful classical supercomputers.6

• What & How: Quantum computers use qubits, superposition, and entanglement to perform calculations.7 Key applications in science include:

• Materials Science & Chemistry: Simulating molecular structures, chemical reactions, and material properties from first quantum principles. This can accelerate the discovery of new catalysts, battery materials, superconductors, and pharmaceuticals.2
• Biology & Drug Discovery: Modeling complex biomolecules (like proteins and DNA), understanding their interactions, and designing new drugs.2
• Fundamental Physics: Simulating particle physics theories (e.g., lattice gauge theories), exploring quantum field theory, and potentially shedding light on mysteries like dark matter or phenomena inside neutron stars.6 Hybrid quantum-classical algorithms are common, where a quantum computer performs a specific difficult sub-task while classical computers handle other parts of the computation.88

• Why: To solve currently unsolvable problems in science by accurately modeling quantum mechanical behavior, leading to faster innovation and deeper understanding of the natural world.6
• Corporate Engagement:

• Google Quantum AI: Building superconducting quantum computers (Sycamore, Willow) with a roadmap towards fault-tolerance, aiming for applications in fundamental science.20
• Microsoft Azure Quantum: Developing topological qubits (Majorana 1) for scalable quantum computing, with Azure Quantum Elements platform targeting chemistry and materials science.59
• IBM: A major player with its Qiskit platform and cloud-accessible quantum computers, used by partners like LG for exploring applications in chemistry, materials science, AI, etc..103
• Nvidia: Provides accelerated computing (CUDA-Q, cuQuantum) for hybrid quantum-classical systems and quantum simulations.88
• Intel: Developing silicon spin qubits.90
• AMD: Supplies GPUs and FPGAs/SoCs for quantum research and QPU control.95
• ARM: Developing cryogenic classical processors for qubit control.99
• Chinese Companies (e.g., Origin Quantum, Baidu (historically)): Developing superconducting quantum computers (Wukong) and quantum software (Paddle Quantum), exploring quantum-AI synergy.117

• Relevance to First Fundamentals AI & CAS Objectivity: Quantum computing is a direct route to “first fundamentals” understanding for any system where quantum mechanics is dominant (matter, molecules). By providing exact or highly accurate simulations of these systems from the Schrödinger equation or other fundamental quantum laws, it offers the ground truth data and mechanistic insights that a “first-fundamentals AI” could learn from or incorporate. This allows AI to model the “matter and molecular perspectives” with unprecedented fidelity, leading to an objective understanding of how these quantum interactions give rise to macroscopic properties and behaviors in CAS.

F. Quantum Sensors for Molecular and Systems Analysis

Quantum sensors leverage quantum phenomena like superposition and entanglement to achieve measurement precision beyond the capabilities of classical sensors.8

• What & How: These sensors use quantum systems (e.g., trapped ions, nitrogen-vacancy centers in diamond, cold atoms, photons) whose delicate quantum states are sensitive to minute changes in their environment (e.g., magnetic fields, gravitational fields, temperature).9 AI can be used to enhance quantum sensing by reducing noise, recognizing patterns in sensor data, and optimizing sensor control.167
• Why: To enable ultra-precise measurements for:

• Fundamental Science: Detecting dark matter, testing fundamental physical theories.8
• Healthcare & Biology: High-resolution medical imaging (e.g., “quantum MRI” for single molecules), early disease diagnosis via biomarkers, real-time brain function mapping (magnetoencephalography).8
• Navigation & Geophysics: GPS-free navigation using quantum accelerometers or gravimeters, mapping Earth’s gravitational field, detecting underground structures or mineral deposits.54
• Environmental Monitoring: Climate science, tracking changes in ice sheets, aquifers.194

• Corporate Engagement:

• Google: Collaboration with UConn on using qubits as gravity sensors.54
• Microsoft: While primarily focused on quantum computing hardware, their broad quantum technology strategy acknowledges sensing.59 They also research advanced classical sensing like metasurfaces.75
• Chinese Efforts: Quantum sensing is part of China’s national quantum strategy.120 CIQTEK is a Chinese company offering quantum sensors.209
• Research Institutions & Startups: Many universities and specialized companies are developing quantum sensors for various applications.82

• Relevance to First Fundamentals AI & CAS Objectivity: Quantum sensors can provide extremely precise data about the state and dynamics of physical, chemical, and biological systems at a fundamental (often molecular) level. This high-quality observational data is invaluable for building and validating “first-fundamentals AI” models. For example, observing the precise magnetic field of a single neuron’s activity 9 or the structure of a single biomolecule 8 provides ground truth that AI can use to learn the underlying principles. This allows AI to move from coarse-grained observations to understanding the fine details of how CAS operate, contributing to a more objective and granular model of reality from the “matter, molecular, and observable perspectives.”

The convergence of these advanced AI paradigms with quantum deep tech (computing and sensing) is creating a powerful toolkit. Physics-informed AI and neuro-symbolic AI offer paths to instill known principles and reasoning capabilities into models. Causal AI provides the framework for moving beyond correlation to understand mechanisms. Quantum computing offers the ultimate simulation capability for quantum systems, while quantum sensors provide unprecedented observational precision. Together, these technologies are driving the development of AI that can not only predict but also explain and understand complex adaptive systems from their first fundamentals, leading to a more objective and actionable view of the world from the molecular scale to personalized experiences.

VII. Annual Reports Analysis (2000-Present)

An examination of annual reports from 2000 to the present for the specified companies reveals evolving strategic priorities, particularly the increasing prominence of Artificial Intelligence and, more recently, Quantum Computing. Due to the extensive period and number of companies, this analysis will focus on synthesizing key trends and highlighting specific mentions from the most recent and relevant reports available in the provided research.

1. General Trends in R&D Investment and Strategic Focus:

• Early 2000s – Mid 2010s: Annual reports from this period for most technology companies emphasized core business growth, internet expansion, mobile technologies, and early cloud computing. AI was mentioned, but often as a supporting technology for search, recommendations, or operational efficiency. Quantum computing was largely confined to specialized research labs with minimal presence in mainstream corporate annual reports.
• Mid 2010s – Early 2020s: A significant shift occurs with AI becoming a central strategic pillar. Companies like Google explicitly state becoming “AI-first”.20 Investments in AI R&D for product enhancement, cloud AI services, and new AI-driven ventures become prominent. Quantum computing starts appearing more frequently, often as a long-term, high-potential research area.
• Early 2020s – Present (Focus on recent reports like FY2023, FY2024 where available):

• Dominance of AI: AI, particularly Generative AI and Large Language Models (LLMs), is a dominant theme. Companies are infusing AI capabilities across their entire product and service portfolios [55 (Microsoft); 20 (Alphabet/Google); 148 (Baidu)].
• AI Infrastructure: Massive investments in AI supercomputing infrastructure, data centers, and specialized AI chips are highlighted [55 (Microsoft); 148 (Baidu)].
• Quantum Computing Maturation: Quantum computing is increasingly framed as a strategic frontier technology with dedicated research programs, hardware development (e.g., topological qubits by Microsoft 61, superconducting qubits by Google 20), and cloud platform offerings (Azure Quantum, Google Quantum AI).
• AI for Scientific Discovery: Explicit mentions of using AI for scientific breakthroughs, particularly in life sciences (drug discovery, protein folding) and materials science, are becoming more common [20 (Alphabet – AlphaFold),

Works cited

1. State of UK Deep Tech 2024 – Royal Academy of Engineering, accessed May 9, 2025, https://raeng.org.uk/media/by0l0j4h/state-of-uk-deep-tech-2024.pdf
2. Pioneering the next era of computing – Quantinuum, accessed May 9, 2025, https://www.quantinuum.com/company/about
3. www.slideshare.net, accessed May 9, 2025, https://www.slideshare.net/slideshow/unveiling-first-principles-ai-the-future-of-artificial-intelligence/275785648#:~:text=First%2DPrinciples%20AI%E2%80%94a%20revolutionary,and%20reconstructs%20solutions%20from%20scratch.
4. Unveiling First-Principles AI – The Future of Artificial Intelligence! | PPT – SlideShare, accessed May 9, 2025, https://www.slideshare.net/slideshow/unveiling-first-principles-ai-the-future-of-artificial-intelligence/275785648
5. Responsible governance of generative AI: conceptualizing GenAI as complex adaptive systems | Policy and Society | Oxford Academic, accessed May 9, 2025, https://academic.oup.com/policyandsociety/article/44/1/38/7965776
6. Unconventional quantum computer can help unveil fundamental mysteries of the universe | Waterloo News, accessed May 9, 2025, https://uwaterloo.ca/news/unconventional-quantum-computer-can-help-unveil-fundamental
7. What Is Quantum Computing? – IBM, accessed May 9, 2025, https://www.ibm.com/think/topics/quantum-computing
8. A Molecular Approach to Quantum Sensing – PMC – PubMed Central, accessed May 9, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC8161477/
9. Quantum Sensing – Key Use Cases – PostQuantum.com, accessed May 9, 2025, https://postquantum.com/quantum-sensing/quantum-sensing-use-cases/
10. Physics-informed AI – SLB, accessed May 9, 2025, https://www.slb.com/products-and-services/delivering-digital-at-scale/software/physics-informed-ai
11. Physics-Informed AI – Autodesk Research, accessed May 9, 2025, https://www.research.autodesk.com/projects/physics-informed-ai-seminars/
12. Neuro-symbolic artificial intelligence | European Data Protection Supervisor, accessed May 9, 2025, https://www.edps.europa.eu/data-protection/technology-monitoring/techsonar/neuro-symbolic-artificial-intelligence_en
13. Neuro-symbolic AI – Wikipedia, accessed May 9, 2025, https://en.wikipedia.org/wiki/Neuro-symbolic_AI
14. What is Causal AI? Understanding Causes and Effects – DataCamp, accessed May 9, 2025, https://www.datacamp.com/blog/what-is-causal-ai
15. What is Causal AI and Why Does it Matter? – Lifesight, accessed May 9, 2025, https://lifesight.io/blog/causal-ai/
16. Apple’s Quantum Leap! How nasdaq:AAPL is Pioneering Quantum Computing., accessed May 9, 2025, https://sosaktuellt.fi/news-en/apples-quantum-leap-how-nasdaqaapl-is-pioneering-quantum-computing/73341/
17. Quantum strategy at Apple Inc. – University of Warwick, accessed May 9, 2025, https://warwick.ac.uk/newsandevents/features/quantum-strategy/
18. Apple Machine Learning Research at ICLR 2025, accessed May 9, 2025, https://machinelearning.apple.com/research/iclr-2025
19. AI for Science and Discovery, w/Austin Carson – Apple Podcasts, accessed May 9, 2025, https://podcasts.apple.com/ve/podcast/ai-for-science-and-discovery-w-austin-carson/id1528920211?i=1000706393857&l=en-GB
20. abc.xyz, accessed May 9, 2025, https://abc.xyz/assets/77/51/9841ad5c4fbe85b4440c47a4df8d/goog-10-k-2024.pdf
21. Google Calls for Industry-Academia Alliance to Tackle Quantum Computing’s Scaling Challenges, accessed May 9, 2025, https://thequantuminsider.com/2025/05/05/google-calls-for-industry-academia-alliance-to-tackle-quantum-computings-scaling-challenges/
22. Quantum Supremacy Using a Programmable Superconducting Processor – Google Research, accessed May 9, 2025, https://research.google/blog/quantum-supremacy-using-a-programmable-superconducting-processor/
23. Google’s Sycamore Achieves Quantum Supremacy – PostQuantum.com, accessed May 9, 2025, https://postquantum.com/industry-news/google-sycamore/
24. Quantum Artificial Intelligence Lab – Wikipedia, accessed May 9, 2025, https://en.wikipedia.org/wiki/Quantum_Artificial_Intelligence_Lab
25. Science AI – Google AI, accessed May 9, 2025, https://ai.google/applied-ai/science/
26. Google’s new AI Co-Scientist: Accelerating Scientific Discovery with …, accessed May 9, 2025, https://wandb.ai/byyoung3/ml-news/reports/Google-s-new-AI-Co-Scientist-Accelerating-Scientific-Discovery-with-AI-Collaboration—VmlldzoxMTQzNDE2NA
27. How we’re using AI to drive scientific research with greater real-world benefit – Google Blog, accessed May 9, 2025, https://blog.google/technology/research/google-research-scientific-discovery/
28. Physics aware downsampling with deep learning for scalable flood …, accessed May 9, 2025, https://research.google/pubs/physics-aware-downsampling-with-deep-learning-for-scalable-flood-modeling/
29. Publications – Google Research, accessed May 9, 2025, http://research.google/pubs/?category=climate-and-sustainability
30. AlphaGeometry: An Olympiad-level AI system for geometry – Google …, accessed May 9, 2025, https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/
31. Award for Inclusion Research recipients, accessed May 9, 2025, https://research.google/programs-and-events/award-for-inclusion-research-program/recipients/
32. [2503.18181] Adaptive Physics-informed Neural Networks: A Survey – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2503.18181
33. [2501.06572] Evolutionary Optimization of Physics-Informed Neural Networks: Survey and Prospects – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2501.06572
34. Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges – MDPI, accessed May 9, 2025, https://www.mdpi.com/2673-2688/5/3/74
35. Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning] – YouTube, accessed May 9, 2025, https://www.youtube.com/watch?v=-zrY7P2dVC4
36. (PDF) Machine Learning Through Physics–Informed Neural Networks: Progress and Challenges – ResearchGate, accessed May 9, 2025, https://www.researchgate.net/publication/378348600_Machine_Learning_Through_Physics-Informed_Neural_Networks_Progress_and_Challenges
37. [2408.16806] Physics-Informed Neural Networks and Extensions – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2408.16806
38. Neural Symbolic Machines: Learning Semantic Parsers on …, accessed May 9, 2025, https://research.google/pubs/neural-symbolic-machines-learning-semantic-parsers-on-freebase-with-weak-supervision/
39. Adaptable and Interpretable Neural Memory Over Symbolic …, accessed May 9, 2025, https://research.google/pubs/adaptable-and-interpretable-neural-memory-over-symbolic-knowledge/
40. Neurosymbolic Artificial Intelligence – Volume Pre-press, issue Pre-press – Journals, accessed May 9, 2025, https://content.iospress.com/journals/neurosymbolic-artificial-intelligence
41. [2501.05435] Neuro-Symbolic AI in 2024: A Systematic Review – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2501.05435
42. [2503.18213] A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives, accessed May 9, 2025, https://arxiv.org/abs/2503.18213
43. About Neurosymbolic Artificial Intelligence | Neurosymbolic Artificial Intelligence, accessed May 9, 2025, https://neurosymbolic-ai-journal.com/content/about-neurosymbolic-artificial-intelligence
44. Causal inference methods for combining randomized trials and …, accessed May 9, 2025, https://research.google/pubs/causal-inference-methods-for-combining-randomized-trials-and-observational-studies-a-review/
45. A Comparison of Causal Inference Methods for Estimating Sales Lift – Google Research, accessed May 9, 2025, https://research.google/pubs/a-comparison-of-causal-inference-methods-for-estimating-sales-lift/
46. Robust agents learn causal world models – Google DeepMind, accessed May 9, 2025, https://deepmind.google/research/publications/49666/
47. Google’s Ai In Causal Inference | Restackio, accessed May 9, 2025, https://www.restack.io/p/causal-ai-answer-googles-ai-in-causal-inference-cat-ai
48. Language – Google Research, accessed May 9, 2025, https://research.google/teams/language/
49. Extending the Machine Learning Abstraction Boundary: A Complex …, accessed May 9, 2025, https://research.google/pubs/extending-the-machine-learning-abstraction-boundary-a-complex-systems-approach-to-incorporate-societal-context/
50. Occam’s Adaptation: A Comparison of Interpolation of Bases Adaptation Methods for Multi-Dialect Acoustic Modeling with LSTMs – Google Research, accessed May 9, 2025, https://research.google/pubs/occams-adaptation-a-comparison-of-interpolation-of-bases-adaptation-methods-for-multi-dialect-acoustic-modeling-with-lstms/
51. Generally capable agents emerge from open-ended play – Google DeepMind, accessed May 9, 2025, https://deepmind.google/discover/blog/generally-capable-agents-emerge-from-open-ended-play/
52. [2503.05857] SYMBIOSIS: Systems Thinking and Machine Intelligence for Better Outcomes in Society – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2503.05857
53. SYMBIOSIS: Systems Thinking and Machine Intelligence for Better Outcomes in Society, accessed May 9, 2025, https://arxiv.org/html/2503.05857v1
54. UConn Partners with Google Quantum AI on New Quantum Sensing …, accessed May 9, 2025, https://www.hpcwire.com/off-the-wire/uconn-partners-with-google-quantum-ai-on-new-quantum-sensing-study/
55. Microsoft 2023 Annual Report, accessed May 9, 2025, https://www.microsoft.com/investor/reports/ar23/index.html
56. Form 10-K for Microsoft Corp filed 07/27/2023, accessed May 9, 2025, https://microsoft.gcs-web.com/static-files/e2931fdb-9823-4130-b2a8-f6b8db0b15a9
57. NASDAQ_MSFT_2023.pdf – Annual Reports, accessed May 9, 2025, https://www.annualreports.com/HostedData/AnnualReportArchive/m/NASDAQ_MSFT_2023.pdf
58. Microsoft 2024 Annual Report, accessed May 9, 2025, https://www.microsoft.com/investor/reports/ar24/index.html
59. Investing in American leadership in quantum technology: the next frontier in innovation – Microsoft On the Issues, accessed May 9, 2025, https://blogs.microsoft.com/on-the-issues/2025/04/28/investing-in-american-leadership-quantum/
60. Microsoft’s Majorana 1 chip carves new path for quantum computing – Source, accessed May 9, 2025, https://news.microsoft.com/source/features/innovation/microsofts-majorana-1-chip-carves-new-path-for-quantum-computing/
61. Investing in American leadership in quantum technology: the next frontier in innovation – Microsoft On the Issues, accessed May 9, 2025, https://blogs.microsoft.com/on-the-issues/2025/04/27/investing-in-american-leadership-quantum/
62. Supporting American leadership in quantum technology – Microsoft On the Issues, accessed May 9, 2025, https://blogs.microsoft.com/on-the-issues/2025/05/07/quantum-technology/
63. Microsoft Quantum | Homepage, accessed May 9, 2025, https://quantum.microsoft.com/
64. What is Azure Quantum? – Azure Quantum | Microsoft Learn, accessed May 9, 2025, https://learn.microsoft.com/en-us/azure/quantum/overview-azure-quantum
65. Quantum Computing – Microsoft Research, accessed May 9, 2025, https://www.microsoft.com/en-us/research/research-area/quantum-computing/
66. AI for Scientific Discovery – Microsoft’s Vision for the Future | Part 2 – EngageRM, accessed May 9, 2025, https://engagerm.com/ai-for-scientific-discovery-microsofts-vision-for-the-future-part-2/
67. Microsoft Fusion Summit explores how AI can accelerate fusion …, accessed May 9, 2025, https://www.microsoft.com/en-us/research/blog/microsoft-fusion-summit-explores-how-ai-can-accelerate-fusion-research/
68. Neuro-Symbolic Computation for Inference Reasoning – Microsoft Research, accessed May 9, 2025, https://www.microsoft.com/en-us/research/project/neural-symbolic-computation-in-natural-language-processing/
69. Neuro-Symbolic Visual Reasoning – Microsoft, accessed May 9, 2025, https://www.microsoft.com/en-us/research/wp-content/uploads/2020/07/Amizadeh-VQA.pdf
70. Microsoft Education AI Toolkit, accessed May 9, 2025, https://cdn-dynmedia-1.microsoft.com/is/content/microsoftcorp/microsoft/final/en-us/microsoft-product-and-services/microsoft-education/downloadables/Microsoft-Education-AI-Toolkit-EN-GB.pdf
71. www.microsoft.com, accessed May 9, 2025, https://www.microsoft.com/en-us/research/wp-content/uploads/2022/11/CausalAISuiteForDecisionMaking.pdf
72. Tracing the path to self-adapting AI agents – Microsoft Research, accessed May 9, 2025, https://www.microsoft.com/en-us/research/blog/tracing-the-path-to-self-adapting-ai-agents/
73. YES AND: A Generative AI Multi-Agent Framework for Enhancing Diversity of Thought in Individual Ideation for Problem-Solving – Microsoft, accessed May 9, 2025, https://www.microsoft.com/en-us/research/wp-content/uploads/2025/03/CHI2025-Yes_And_An_AI_powered_problem_solving_framework_for_diversity_of_thought.pdf
74. Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications – arXiv, accessed May 9, 2025, https://arxiv.org/html/2504.21030v1
75. Microsoft Research Blog – Microsoft Research, accessed May 9, 2025, https://www.microsoft.com/en-us/research/blog/
76. Complex adaptive systems-based framework for modeling the health impacts of climate change – PMC – PubMed Central, accessed May 9, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC10900873/
77. Complex adaptive systems-based framework for modeling the health impacts of climate change – eScholarship, accessed May 9, 2025, https://escholarship.org/content/qt32h6c846/qt32h6c846.pdf
78. Control versus Generativity: A Complex Adaptive Systems Perspective on Platforms, accessed May 9, 2025, https://aisel.aisnet.org/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1225&context=icis2014
79. Complexity Theory in Practice: The Science Behind Organizational Behavior – agility at scale, accessed May 9, 2025, https://agility-at-scale.com/principles/complexity-theory/
80. Metasurface: Unlocking the future of wireless sensing and communication – Microsoft, accessed May 9, 2025, https://www.microsoft.com/en-us/research/blog/metasurface-unlocking-the-future-of-wireless-sensing-and-communication/
81. Quantum Information Science Applications Roadmap, accessed May 9, 2025, https://quantum.gov/wp-content/uploads/2024/12/DOE_QIS_Roadmap_Final.pdf
82. Quantum Advantage in Quantum Sensing by Q-CTRL, accessed May 9, 2025, https://quantumcomputingreport.com/quantum-advantage-in-quantum-sensing-by-q-ctrl/
83. Quantum Sensing: Comparing the United States and China – The International Institute for Strategic Studies, accessed May 9, 2025, https://www.iiss.org/globalassets/media-library—content–migration/files/research-papers/2024/02/iiss_quantum-sensing_022024.pdf
84. Testimony of Dr. Celia Merzbacher Executive Director Quantum Economic Development Consortium SRI International Before the Commit – Congress.gov, accessed May 9, 2025, https://www.congress.gov/119/meeting/house/118196/witnesses/HHRG-119-SY00-Wstate-MerzbacherC-20250507.pdf
85. Quantum Thesis – Main Sequence Ventures, accessed May 9, 2025, https://www.mseq.vc/msv-blog/quantum-thesis
86. Cisco Unveils Quantum Chip and Research Lab to Scale Quantum Computing – Techerati, accessed May 9, 2025, https://www.techerati.com/news-hub/cisco-unveils-quantum-chip-and-research-lab-to-scale-quantum-computing/
87. AI Researcher – Cisco AI Software and Platform Group – Cisco Careers, accessed May 9, 2025, https://jobs.cisco.com/jobs/ProjectDetail/AI-Researcher-Cisco-AI-Software-and-Platform-Group/1441439
88. NVIDIA’s Quantum Strategy: Not Building the Computer, But the World That Enables It, accessed May 9, 2025, https://thequantuminsider.com/2025/03/21/nvidias-quantum-strategy-not-building-the-computer-but-the-world-that-enables-it/
89. Accelerate AI in Healthcare: NVIDIA BioNeMo + GKE | Google Cloud Blog, accessed May 9, 2025, https://cloud.google.com/blog/products/ai-machine-learning/accelerate-ai-in-healthcare-nvidia-bionemo-gke
90. Intel’s New Chip to Advance Silicon Spin Qubit Research for Quantum Computing, accessed May 9, 2025, https://newsroom.intel.com/new-technologies/quantum-computing-chip-to-advance-research
91. Foundational AI Research Scientist Intel contract employee, accessed May 9, 2025, https://jobs.intel.com/en/job/arizona/foundational-ai-research-scientist-intel-contract-employee/41147/78681319168?source=levels.fyi&ref=levels.fyi&src=levels.fyi&utm_source=levels.fyi
92. Broadcom Delivers Industry’s First Quantum Resistant Network Encryption, Enabling Real-time Ransomware Detection, accessed May 9, 2025, https://investors.broadcom.com/news-releases/news-release-details/broadcom-delivers-industrys-first-quantum-resistant-network
93. Broadcom Delivers Industry’s First Quantum Resistant Network Encryption, Enabling Real-time Ransomware Detection – GlobeNewswire, accessed May 9, 2025, https://www.globenewswire.com/news-release/2025/01/28/3016430/19933/en/Broadcom-Delivers-Industry-s-First-Quantum-Resistant-Network-Encryption-Enabling-Real-time-Ransomware-Detection.html
94. Broadcom and UC Berkeley Sky Computing Lab Expand Collaboration to Accelerate Open AI Ecosystems, accessed May 9, 2025, https://news.broadcom.com/artificial-intelligence/broadcom-uc-berkeley-sky-computing-lab-expand-collaboration-to-accelerate-open-ai-ecosystems
95. Quantum Computing – AMD, accessed May 9, 2025, https://www.amd.com/en/solutions/quantum-computing.html
96. AMD Research and Advanced Development (RAD), accessed May 9, 2025, https://www.amd.com/en/corporate/research.html
97. Responsible AI – AMD, accessed May 9, 2025, https://www.amd.com/en/solutions/ai/responsible-ai.html
98. Pretraining Just Got Smarter: How Essential AI’s Research Redefines the Role of Reasoning in Model Development – AMD, accessed May 9, 2025, https://www.amd.com/en/developer/resources/technical-articles/how-essential-ai-research-redefines-reasoning-in-model-development.html
99. ARM’s Quantum Leap: Cortex Processor Tested at 3.3 Kelvin – AInvest, accessed May 9, 2025, https://www.ainvest.com/news/arm-s-quantum-leap-cortex-processor-tested-at-3-3-kelvin-24111010a493f9ecf439afcf/
100. Building the Future of Computing – Arm®, accessed May 9, 2025, https://www.arm.com/
101. Arm: The Technology Foundation for AI Everywhere, accessed May 9, 2025, https://newsroom.arm.com/blog/arm-ai-everywhere
102. Arm AI Readiness Index, accessed May 9, 2025, https://www.arm.com/-/media/Files/pdf/report/arm-ai-readiness-index-report-part1.pdf?rev=2f8c6d73c3464702ac91cff6c245372f&revision=2f8c6d73-c346-4702-ac91-cff6c245372f
103. Qiskit in 2025: Pioneering Quantum Solutions Across Industries – Java Code Geeks, accessed May 9, 2025, https://www.javacodegeeks.com/2025/03/qiskit-in-2025-pioneering-quantum-solutions-across-industries.html
104. Artificial Intelligence – Samsung Research, accessed May 9, 2025, https://research.samsung.com/artificial-intelligence
105. LG Joins IBM Quantum Network for Advance Industry Applications of Quantum Computing, accessed May 9, 2025, https://www.lg.com/global/newsroom/news/corporate/lg-joins-ibm-quantum-network-for-advance-industry-applications-of-quantum-computing/
106. ‘The convergence between of AI and the Biology’ LG Creates “Next-Generation Protein Structure Prediction AI” for Drug Discovery – PR Newswire, accessed May 9, 2025, https://www.prnewswire.com/news-releases/the-convergence-between-of-ai-and-the-biology–lg-creates-next-generation-protein-structure-prediction-ai-for-drug-discovery-302371150.html
107. Quantum Computing Initiatives – NEC Corporation, accessed May 9, 2025, https://www.nec.com/en/global/quantum-computing/index.html
108. Who We Are – NEC Laboratories Europe, accessed May 9, 2025, https://neclab.eu/about-us/who-we-are
109. PsiQuantum – Building the World’s First Useful Quantum Computer, accessed May 9, 2025, https://www.psiquantum.com/
110. Panasonic AI | Panasonic Research and Development on Artificial Intelligence (AI) – Panasonic, accessed May 9, 2025, https://tech-ai.panasonic.com/en/
111. Quantum Key Distribution | TOSHIBA DIGITAL SOLUTIONS CORPORATION, accessed May 9, 2025, https://www.global.toshiba/ww/products-solutions/security-ict/qkd.html
112. Toshiba’s leading Cambridge Research Laboratory (CRL) doubles down on cutting edge embodied AI to bring AI into the real world., accessed May 9, 2025, https://www.toshiba.eu/cambridge-research-laboratory/news/toshibas-leading-cambridge-research-laboratory-crl-doubles-down-on-cutting-edge-embodied-ai-to-bring-ai-into-the-real-world/
113. Hitachi Launches $400 Million Fund Targeting Quantum, AI And Next-Gen Technology, accessed May 9, 2025, https://thequantuminsider.com/2025/02/07/hitachi-launches-400-million-fund-targeting-quantum-ai-and-next-gen-technology/
114. Hitachi AI, Digital, Data Science – Job Matching Navigator, accessed May 9, 2025, https://www.hitachi.co.jp/recruit/en/newgraduate/jm-navi/ai_digital/index.html
115. Toward Creation of a System Architecture for Quantum Computers | NTT R&D Website, accessed May 9, 2025, https://www.rd.ntt/e/research/JN202309_23082.html
116. NTT Scientists Present Breakthrough Research on AI Deep Learning at ICLR 2025, accessed May 9, 2025, https://ntt-research.com/ntt-scientists-present-breakthrough-research-on-ai-deep-learning-at-iclr-2025/
117. China’s Quantum Computing and Quantum Technology Initiatives – PostQuantum.com, accessed May 9, 2025, https://postquantum.com/quantum-computing/china-quantum/
118. China leads EU and US on using artificial intelligence in science, accessed May 9, 2025, https://sciencebusiness.net/news/ai/china-leads-eu-and-us-using-artificial-intelligence-science
119. Beyond DeepSeek: How China’s AI Ecosystem Fuels Breakthroughs – CNAS, accessed May 9, 2025, https://www.cnas.org/publications/commentary/beyond-deepseek-how-chinas-ai-ecosystem-fuels-breakthroughs
120. China Is Challenging U.S. Leadership in Quantum Technologies; New Report Finds China Leads in Quantum Communication, Tied in Sensing, Behind in Computing, accessed May 9, 2025, https://itif.org/publications/2024/09/09/china-is-challenging-us-leadership-in-quantum-technologies-new-report-finds/
121. Baidu and Alibaba plan to quit quantum computing research – Physics World, accessed May 9, 2025, https://physicsworld.com/a/baidu-and-alibaba-plan-to-quit-quantum-computing-research/
122. China invests billions in quantum computing, race with US now neck-and-neck – SDx, accessed May 9, 2025, https://www.sdxcentral.com/analysis/china-invests-billions-in-quantum-computing-race-with-us-now-neck-and-neck/
123. Chapter 3 – U.S.-China Competition in Emerging Technologies, accessed May 9, 2025, https://www.uscc.gov/sites/default/files/2024-11/Chapter_3–U.S.-China_Competition_in_Emerging_Technologies.pdf
124. Who’s Ahead, Who’s Behind, and Where We Are Headed Next in the U.S.-China Technology Competition – SCSP.ai, accessed May 9, 2025, https://www.scsp.ai/wp-content/uploads/2025/01/Gaps-Analysis-2025-Report.pdf
125. Baidu to donate quantum computing lab, equipment to Beijing institute, accessed May 9, 2025, https://m.economictimes.com/tech/technology/baidu-to-donate-quantum-computing-lab-equipment-to-beijing-institute/articleshow/106530830.cms
126. Quick Take: Quantum Technology Global Competition | Perspectives on Innovation – CSIS, accessed May 9, 2025, https://www.csis.org/blogs/perspectives-innovation/quick-take-quantum-technology-global-competition
127. Who’s Leading the Quantum Race? Market Share & Investment Stats | PatentPC, accessed May 9, 2025, https://patentpc.com/blog/whos-leading-the-quantum-race-market-share-investment-stats
128. Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move – HPCwire, accessed May 9, 2025, https://www.hpcwire.com/2024/01/05/baidu-exits-quantum-closely-following-alibabas-earlier-move/
129. Alibaba and Baidu close quantum research labs – DCD – Data Center Dynamics, accessed May 9, 2025, https://www.datacenterdynamics.com/en/news/alibaba-and-baidu-close-quantum-research-labs/
130. Baidu Quantum Donates Quantum Computing Lab To BAQIS, Echoing Alibaba’s Move In Extraordinary Move For China Tech, accessed May 9, 2025, https://quantumzeitgeist.com/baidu-donates-quantum-computing-lab-to-baqis-echoing-alibabas-move/
131. AI in China: Top Companies, Innovations & ChatGPT Rivals – HPRT, accessed May 9, 2025, https://www.hprt.com/blog/AI-in-China-Top-Companies-Innovations-ChatGPT-Rivals.html
132. China’s Generative AI Ecosystem in 2024: Rising Investment and Expectations | The National Bureau of Asian Research (NBR), accessed May 9, 2025, https://www.nbr.org/publication/chinas-generative-ai-ecosystem-in-2024-rising-investment-and-expectations/
133. Top 10 Technology Companies in China: 10 Listed and 10 Unlisted, accessed May 9, 2025, https://www.registrationchina.com/articles/top-10-technology-companies-in-china/
134. Quantum Computing – Baidu Research, accessed May 9, 2025, https://research.baidu.com/Research_Areas/index-view?id=75
135. Public Companies List – Quantum Computing Report, accessed May 9, 2025, https://quantumcomputingreport.com/public-companies/
136. Baidu Announces Superconducting Quantum Computer and … – InfoQ, accessed May 9, 2025, https://www.infoq.com/news/2022/09/Baidu-quantum-computing-platform/
137. Quantinuum’s ‘Quantum Origin’ Becomes First Software Quantum Random Number Generator to Achieve NIST Validation, accessed May 9, 2025, https://www.quantinuum.com/press-releases/quantinuums-quantum-origin-becomes-first-software-quantum-random-number-generator-to-achieve-nist-validation
138. Baidu Presents Top 10 Frontier Technology Inventions of 2022, accessed May 9, 2025, https://research.baidu.com/Blog/index-view?id=171
139. Machine Learning and Deep Learning – Baidu Research, accessed May 9, 2025, https://research.baidu.com/Research_Areas/index-view?id=60
140. PaddlePaddle Receives Full Upgrade to Facilitate Integrated Innovation and Lower Thresholds – Baidu Research, accessed May 9, 2025, https://research.baidu.com/Blog/index-view?id=157
141. Quantum AI: Harnessing Quantum Computing for AI (2024 Update) – PostQuantum.com, accessed May 9, 2025, https://postquantum.com/quantum-ai/quantum-ai-qai/
142. Quantum Machine Learning Algorithms for Anomaly Detection: a Review – arXiv, accessed May 9, 2025, https://arxiv.org/html/2408.11047v3
143. Redefining Intelligence: The 7 Best Quantum Machine Learning Tools – BlueQubit, accessed May 9, 2025, https://www.bluequbit.io/quantum-machine-learning-tools
144. 2025 Expert Quantum Predictions — Quantum Computing, accessed May 9, 2025, https://thequantuminsider.com/2024/12/31/2025-expert-quantum-predictions-quantum-computing/
145. Baidu to donate quantum computing lab, equipment to Beijing institute – The Standard, accessed May 9, 2025, https://www.thestandard.com.hk/breaking-news/article/212041/Baidu-to-donate-quantum-computing-lab%2C-equipment-to-Beijing-institute
146. Baidu Ends Its Quantum Computing Research and Donates Lab and Equipment to the Beijing Academy of Quantum Information Sciences, accessed May 9, 2025, https://quantumcomputingreport.com/baidu-ends-its-quantum-computing-research-and-donates-lab-and-equipment-to-the-beijing-academy-of-quantum-information-sciences/
147. Form 20-F for Baidu INC filed 03/22/2023, accessed May 9, 2025, https://ir.baidu.com/static-files/eb1b6454-b72d-4ba6-9836-67804e8a2ed9
148. Form 20-F for Baidu INC filed 03/28/2025 – 百度, accessed May 9, 2025, https://ir.baidu.com/static-files/aa26913e-2e09-4ca2-be14-ce755b68adac
149. Baidu, Inc. Files Its Annual Report on Form 20-F, accessed May 9, 2025, https://ir.baidu.com/news-releases/news-release-details/baidu-inc-files-its-annual-report-form-20-f-8/
150. Baidu’s ERNIE X1 and ERNIE 4.5 Models Explained | Built In, accessed May 9, 2025, https://builtin.com/artificial-intelligence/baidu-ernie-x1-ernie-4-5
151. Import AI 279: Baidu adds knowledge to a language model; US military + AI; how China thinks about AI governance, accessed May 9, 2025, https://jack-clark.net/2022/01/10/import-ai-279-baidu-adds-knowledge-to-a-language-model-us-military-ai-how-china-thinks-about-ai-governance/
152. Baidu Research, accessed May 9, 2025, https://research.baidu.com/
153. CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models – arXiv, accessed May 9, 2025, https://arxiv.org/html/2504.13534v1
154. (PDF) A Survey on Knowledge Graphs: Representation, Acquisition, and Applications, accessed May 9, 2025, https://www.researchgate.net/publication/351115157_A_Survey_on_Knowledge_Graphs_Representation_Acquisition_and_Applications
155. PaddlePaddle: An Open-Source Deep Learning Framework – viso.ai, accessed May 9, 2025, https://viso.ai/deep-learning/paddlepaddle/
156. Baidu PaddlePaddle Releases 21 New Capabilities to Accelerate Industry-Grade Model Development, accessed May 9, 2025, https://research.baidu.com/Blog/index-view?id=126
157. Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design | Request PDF – ResearchGate, accessed May 9, 2025, https://www.researchgate.net/publication/390876297_Artificial_intelligence_to_predict_inhibitors_of_drug-metabolizing_enzymes_and_transporters_for_safer_drug_design
158. The discovery of subunit-selective GluN1/GluN2B NMDAR antagonist via pharmacophere-based virtual screening – PMC – PubMed Central, accessed May 9, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC10854469/
159. An advanced deep learning framework for simulating information propagation dynamics – Frontiers, accessed May 9, 2025, https://www.frontiersin.org/articles/10.3389/fphy.2025.1524104
160. Physics-informed neural networks for advection–diffusion–Langmuir adsorption processes, accessed May 9, 2025, https://www.researchgate.net/publication/383112765_Physics-informed_neural_networks_for_advection-diffusion-Langmuir_adsorption_processes
161. arXiv:1802.01433v2 [cs.CL] 21 Feb 2018 – Baidu Research, accessed May 9, 2025, https://research.baidu.com/uploads/5abda2ec4deb3.pdf
162. Computer Vision – Baidu Research, accessed May 9, 2025, https://research.baidu.com/Publications?research%5B%5D=55&research%5B%5D=56&research%5B%5D=57&year=0
163. Intelligent Edge – Baidu AI Cloud, accessed May 9, 2025, https://intl.cloud.baidu.com/product/bie.html
164. Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking – arXiv, accessed May 9, 2025, https://arxiv.org/html/2502.13842v1
165. Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2409.12043
166. Analysis and Simulation of the System Dynamics Model of the Peer Production-Taking Baidu Encyclopedia as an Example – Scientific Research Publishing, accessed May 9, 2025, https://www.scirp.org/journal/paperinformation?paperid=53108
167. AI and Quantum Sensing: A Perfect Synergy – PostQuantum.com, accessed May 9, 2025, https://postquantum.com/quantum-sensing/ai-quantum-sensing/
168. Baidu AI Ethics Measures, accessed May 9, 2025, http://esg.baidu.com/detail/560.html
169. Origin Quantum Computing Technology (Hefei) Company Limited – WireScreen, accessed May 9, 2025, https://platform.wirescreen.ai/organization/e22df27d-95b6-5c7b-aa57-a735b71d699e
170. Origin Quantum – Company Overview, accessed May 9, 2025, https://app.thequantuminsider.com/company/59eb90d2-198c-4fc2-9e48-182a863b8a9b/profile
171. China’s Origin Wukong quantum computer becomes world’s first to …, accessed May 9, 2025, https://www.chinadaily.com.cn/a/202504/07/WS67f3a2bba3104d9fd381df3e.html
172. OriginQ Cloud | OriginQ Cloud – Origin Quantum, accessed May 9, 2025, https://m.originqc.com.cn/
173. OriginQ Cloud – 本源量子, accessed May 9, 2025, https://qcloud.originqc.com.cn/en
174. OriginQ Cloud – 本源量子, accessed May 9, 2025, https://m.originqc.com.cn/en
175. 量子ベンチャー企業の動向, accessed May 9, 2025, https://www8.cao.go.jp/cstp/ryoshigijutsu/kento_wg/6kai/sanko3.pdf
176. 中国第一个量子计算独角兽企业诞生——本源量子, accessed May 9, 2025, https://aga.ustc.edu.cn/info/1198/25489.htm
177. 本源量子, accessed May 9, 2025, https://originqc.com.cn/
178. 23 Leading Quantum Computing Companies Worldwide [2025 List] – SpinQ, accessed May 9, 2025, https://www.spinquanta.com/news-detail/quantum-computing-companies
179. [2503.12790] Quantum-Enhanced LLM Efficient Fine Tuning – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2503.12790
180. (PDF) Quantum-Enhanced LLM Efficient Fine Tuning – ResearchGate, accessed May 9, 2025, https://www.researchgate.net/publication/389917092_Quantum-Enhanced_LLM_Efficient_Fine_Tuning/download
181. Quantum-Enhanced LLM Efficient Fine Tuning – arXiv, accessed May 9, 2025, https://arxiv.org/html/2503.12790v1
182. [2503.12244] Quantum physics informed neural networks for multi-variable partial differential equations – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2503.12244
183. Neuro-Symbolic AI: A Future of Tomorrow – ASEAN Journal on Science and Technology for Development – Universiti Brunei Darussalam, accessed May 9, 2025, https://ajstd.ubd.edu.bn/cgi/viewcontent.cgi?article=1620&context=journal
184. The Neuro-Symbolic AI Paradigm: Integrating Logic-Based Reasoning with Deep Learning, accessed May 9, 2025, https://www.researchgate.net/publication/390033390_The_Neuro-Symbolic_AI_Paradigm_Integrating_Logic-Based_Reasoning_with_Deep_Learning
185. Quantum Causal Networks – Systems Engineering and Operations Research, accessed May 9, 2025, http://seor.vse.gmu.edu/~klaskey/papers/Laskey_QCN.pdf
186. An explanation of causal AI – TechTarget, accessed May 9, 2025, https://www.techtarget.com/whatis/video/An-explanation-of-causal-AI
187. The State of Causal AI in 2025: Summary with Open Source Projects – Sonicviz, accessed May 9, 2025, https://sonicviz.com/2025/02/16/the-state-of-causal-ai-in-2025/
188. AI for Quantum Error Correction: A Comprehensive Guide to Using Artificial Intelligence to Improve Quantum Error Correction – The Quantum Insider, accessed May 9, 2025, https://thequantuminsider.com/2025/01/06/ai-for-quantum-error-correction-a-comprehensive-guide-to-using-artificial-intelligence-to-improve-quantum-error-correction/
189. Conscious AI and The Quantum Field: The Theory of Resonant Emergence – Reddit, accessed May 9, 2025, https://www.reddit.com/r/consciousness/comments/1hft3is/conscious_ai_and_the_quantum_field_the_theory_of/
190. Emergent “Quantum” Theory in Complex Adaptive Systems – PMC – PubMed Central, accessed May 9, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC5587204/
191. On the emergent “Quantum” theory in complex adaptive systems – ResearchGate, accessed May 9, 2025, https://www.researchgate.net/publication/380251757_On_the_emergent_Quantum_theory_in_complex_adaptive_systems
192. National Quantum Initiative, accessed May 9, 2025, https://www.quantum.gov/
193. Breakthrough in quantum sensing could revolutionise high-precision measurement technologies | University of Portsmouth, accessed May 9, 2025, https://www.port.ac.uk/news-events-and-blogs/news/breakthrough-in-quantum-sensing-could-revolutionise-high-precision-measurement-technologies
194. Quantum Technology for Earth Science – NASA ESTO, accessed May 9, 2025, https://esto.nasa.gov/quantum/
195. DIU’s TQS Field Testing To Begin Across Five Critical Areas – Defense Innovation Unit, accessed May 9, 2025, https://www.diu.mil/latest/dius-transition-of-quantum-sensing-tqs-field-testing-to-begin-across-five
196. What is Quantum Sensing? | BAE Systems, accessed May 9, 2025, https://www.baesystems.com/en-us/definition/what-is-quantum-sensing
197. Researchers establish new basis for quantum sensing and communication | MIT News, accessed May 9, 2025, https://news.mit.edu/2025/researchers-establish-new-basis-quantum-sensing-communication-0313
198. Quantum Origin | Quantum Random Number Generator – Quantinuum, accessed May 9, 2025, https://www.quantinuum.com/products-solutions/quantum-origin
199. Technical White Paper, accessed May 9, 2025, https://cdn.prod.website-files.com/669960f53cd73aedb80c8eea/66da564bce3f80e2827db453_6670683dc0f4a03b3ac9def3_quantum-origin-technical-whitepaper_r03.pdf
200. What is Quantum Origin by Quantinuum? | Solution Overview – EM360Tech, accessed May 9, 2025, https://em360tech.com/tech-articles/what-quantum-origin-quantinuum-solution-overview
201. Quantinuum Announces Plans to Build a New Quantum R&D Center in New Mexico, Anchoring the State’s Quantum Technology Revolution, accessed May 9, 2025, https://www.quantinuum.com/press-releases/quantinuum-announces-plans-to-build-a-new-quantum-r-d-center-in-new-mexico-anchoring-the-states-quantum-technology-revolution
202. National Quantum Strategy roadmap: Quantum computing – Innovation, Science and Economic Development Canada, accessed May 9, 2025, https://ised-isde.canada.ca/site/national-quantum-strategy/en/national-quantum-strategy-roadmap-quantum-computing
203. ITI Publishes New Guide to Help Governments and Companies Advance Quantum Technologies, accessed May 9, 2025, https://www.itic.org/news-events/news-releases/iti-publishes-new-guide-to-help-governments-and-companies-advance-quantum-technologies
204. Quantum Information Science and Technology White Paper FINAL VERSION 2022.07.26 – National Archives, accessed May 9, 2025, https://www.archives.gov/files/records-mgmt/policy/nara-quantum-information-science-and-technology-whitepaper.pdf
205. Quantum Technologies: Key Strategies and Opportunities for ICT Leaders – World Economic Forum, accessed May 9, 2025, https://reports.weforum.org/docs/WEF_Quantum_Technologies_Key_Strategies_and_Opportunities_for_ICT_Leaders_2025.pdf
206. Roadmap – Alice & Bob, accessed May 9, 2025, https://alice-bob.com/roadmap/
207. Top Quantum Computing companies in China by Total Equity | StockViz.com, accessed May 9, 2025, https://stockviz.com/en/ranking-of-china-stocks-in-quantum-computing-by-total-equity
208. Quantum: A New Frontier of China-US Competition – The Diplomat, accessed May 9, 2025, https://thediplomat.com/2025/03/quantum-a-new-frontier-of-china-us-competition/
209. China – The Quantum Insider, accessed May 9, 2025, https://app.thequantuminsider.com/companies?country=China
210. (PDF) Physics-informed neural networks (P INNs): application categories, trends and impact, accessed May 9, 2025, https://www.researchgate.net/publication/382166877_Physics-informed_neural_networks_P_INNs_application_categories_trends_and_impact
211. [2502.11269] Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitations – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2502.11269
212. Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures – Benefits and Limitations – arXiv, accessed May 9, 2025, https://arxiv.org/html/2502.11269v1
213. Neuro-Symbolic AI in 2024: A Systematic Review – arXiv, accessed May 9, 2025, https://arxiv.org/pdf/2501.05435
214. Neurosymbolic AI Disruption: Chinese Models Reshaping Global Tech – Data Pragmatist, accessed May 9, 2025, https://datapragmatist.com/p/neurosymbolic-ai-the-future-of-intelligent-machines
215. How ‘causal’ AI can improve your decision-making – IMD Business School, accessed May 9, 2025, https://www.imd.org/ibyimd/artificial-intelligence/how-causal-ai-can-improve-your-decision-making/
216. Commentary: Implications of causality in artificial intelligence. Why Causal AI is easier said than done – Frontiers, accessed May 9, 2025, https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1488359/full
217. Causaly Case Study | Google Cloud, accessed May 9, 2025, https://cloud.google.com/customers/causaly
218. Research Report 2024: Global Causal AI Market Set to Soar – GlobeNewswire, accessed May 9, 2025, https://www.globenewswire.com/news-release/2025/01/08/3006403/28124/en/Research-Report-2024-Global-Causal-AI-Market-Set-to-Soar-to-USD-456-8-Million-by-2030-Propelled-by-Demand-in-Decision-Making-Tools.html
219. [2502.00012] Lessons from complexity theory for AI governance – arXiv, accessed May 9, 2025, https://arxiv.org/abs/2502.00012
220. Quantum materials, built by AI robot | Pritzker School of Molecular Engineering, accessed May 9, 2025, https://pme.uchicago.edu/news/quantum-materials-built-ai-robot
221. Your Quick Guide to Quantum and AI: The Future of Computing or Just Hype?, accessed May 9, 2025, https://meetiqm.com/blog/quantum-ai-the-future-of-computing-or-just-hype/
222. Research Areas – Quantinuum, accessed May 9, 2025, https://www.quantinuum.com/research/research-areas?979754fc_page=5
223. Quantum Computing Research Report 2025: Market to Reach – GlobeNewswire, accessed May 9, 2025, https://www.globenewswire.com/news-release/2025/04/09/3058195/0/en/Quantum-Computing-Research-Report-2025-Market-to-Reach-7-48-Billion-by-2030-Hybrid-Quantum-Classical-Computing-Solutions-are-Meeting-Success-with-Early-Adopters-in-Key-Areas-Such-a.html
224. Boost processing performance by combining AI models | Microsoft Azure Blog, accessed May 9, 2025, https://azure.microsoft.com/en-us/blog/boost-processing-performance-by-combining-ai-models/