Rediscovering Quantum Mechanics Through Historically Constrained Artificial Intelligence: A Research Proposal on Emergent Scientific Reasoning and Theory Formation
This proposal outlines a research program designed to investigate, under controlled conditions, whether an advanced artificial reasoning system — when restricted to the scientific knowledge available before the formal emergence of quantum mechanics — can independently arrive at theoretical structures equivalent to, or distinct from, those developed by Planck, Bohr, Heisenberg, Schrödinger, Born, Dirac and their contemporaries. The project sits at the intersection of automated scientific discovery, philosophy of science, and the foundations of quantum theory. The central scientific question is whether the conceptual core of quantum mechanics — discreteness, superposition, operator formalism, probabilistic prediction — is uniquely fixed by the empirical anomalies that motivated it, or whether mathematically distinct but empirically adequate frameworks remain available in the space of theories. Rather than attempting a full pretraining-from-scratch (which is computationally and empirically infeasible at frontier-model scale), the program proposes a tractable, staged methodology combining curated historical corpora, symbolic regression, and constrained agentic reasoning systems. Three deliverables are targeted: (i) a reproducible, leakage-audited historical scientific corpus; (ii) an empirical study of theory-formation behavior across model classes; and (iii) a comparative analysis against the historical record and against contemporary AI4Science benchmarks.
This proposal outlines a research program designed to investigate, under controlled conditions, whether an advanced artificial reasoning system — when restricted to the scientific knowledge available before the formal emergence of quantum mechanics — can independently arrive at theoretical structures equivalent to, or distinct from, those developed by Planck, Bohr, Heisenberg, Schrödinger, Born, Dirac and their contemporaries. The project sits at the intersection of automated scientific discovery, philosophy of science, and the foundations of quantum theory. The central scientific question is whether the conceptual core of quantum mechanics — discreteness, superposition, operator formalism, probabilistic prediction — is uniquely fixed by the empirical anomalies that motivated it, or whether mathematically distinct but empirically adequate frameworks remain available in the space of theories. Rather than attempting a full pretraining-from-scratch (which is computationally and empirically infeasible at frontier-model scale), the program proposes a tractable, staged methodology combining curated historical corpora, symbolic regression, and constrained agentic reasoning systems. Three deliverables are targeted: (i) a reproducible, leakage-audited historical scientific corpus; (ii) an empirical study of theory-formation behavior across model classes; and (iii) a comparative analysis against the historical record and against contemporary AI4Science benchmarks.
Artificial Intelligence for Quantum Discovery:From State Reconstruction to Hamiltonian Learning and Post-Quantum Law Extraction Artificial Intelligence is rapidly evolving from a computational accelerator into ascientific discovery mechanism. Quantum experiments now generate large-scaledatasets — entanglement snapshots, Hamiltonian trajectories, control feedback traces —which exceed the dimensional and conceptual capacity of human analytical methods. This paper presents a unified framework in which AI does not merely simulatequantum systems, but discovers their structure. We review and systematize recentresults in: • neural-network quantum tomography,• NQS variational wavefunction learning,• unsupervised discovery of quantum phases and materials,• Hamiltonian learning from dynamical evolution,• reinforcement-learning quantum control, and• AI-driven extraction of Schrödinger and Born from data. We introduce a law-recovery engine that derives quantum dynamics from experimentalmeasurement statistics via symbolic complexity minimization, and then extend theframework into post-quantum anomaly detection, enabling AI to identify deviationsthat could indicate new physical law. If quantum mechanics is complete, this pipeline will verify it at unprecedentedresolution.If it is not, AI may be the first method capable of revealing what lies beyond it. 🔑 Keywords (Zenodo / arXiv Optimized) Primary indexing terms quantum machine learning (QML) neural quantum states (NQS) Hamiltonian learning quantum state tomography reinforcement learning for quantum control AI-assisted phase discovery Law discovery / foundational physics Schrödinger law emergence Born rule recovery post-quantum anomaly detection symbolic regression in physics discovery of physical laws Experimental relevance Rydberg atom arrays superconducting qubits trapped ions photonic interferometry many-body quantum systems Meta-themes artificial intelligence for science automated theory formation machine-generated physics data-driven quantum foundations
High Resolution Image Download MS PowerPoint Slide In July 1925, Werner Heisenberg submitted a paper to Zeitschrift für Physik entitled ‘On quantum-theoretical reinterpretation of kinematic and mechanical relationships’, thus giving birth to quantum mechanics. In the following year, building on de Broglie’s wave-particle duality, Erwin Schrödinger developed wave mechanics, and soon, Max Born provided a probabilistic interpretation of the wave function. The theory was further enriched by the exclusion principle of Wolfgang Pauli and the uncertainty principle of Heisenberg, which ultimately led to the development of relativistic quantum mechanics by Dirac. The Copenhagen Interpretation created a probabilistic framework for understanding the theory. Over the past century, quantum mechanics has paved the way for advances in quantum field theory, computing, and modern technologies. This historical narrative provides insights into the complex discovery process that led to the development of quantum mechanics, which can potentially guide novel breakthroughs amid challenging conceptual struggles, as seen in the field of artificial intelligence today.
This paper presents a novel interdisciplinary framework that integrates quantum supersymmetry with artificial intelligence to develop an algebraically structured foundation for generalized intelligence. Drawing from the mathematical rigor of supersymmetric quantum mechanics and Lie superalgebras such as osp(1∣2) and sl(2∣1), we propose AI architectures where learning dynamics are governed by graded algebraic transformations and symmetry-preserving constraints. By modeling intelligent agents as evolving quantum systems under the action of supercharges, the framework introduces dual representations—bosonic and fermionic—into latent spaces, enabling semantically coherent, interpretable, and generalizable learning. The research outlines a hybrid simulation environment combining symbolic computation and quantum frameworks to validate superalgebra-guided models on symbolic reasoning and structured prediction tasks. This approach not only enhances model robustness and generalization but also opens new frontiers in quantum-algebraic AI, offering a blueprint for the design of principled, high-order intelligence. Keywords: quantum supersymmetry, Lie superalgebras, generalized intelligence, artificial intelligence, algebraic AI, osp(1∣2), symmetry-preserving learning, supercharges, graded vector spaces, quantum AI, interpretability, symbolic reasoning, scientific discovery, algebraic machine learning, Hamiltonian learning
The simulation of quantum matter with classical hardware plays a central role in the discovery and development of quantum many-body systems, with far-reaching implications in condensed matter physics and quantum technologies. In general, efficient and sophisticated algorithms are required to overcome the severe challenge posed by the exponential scaling of the Hilbert space of quantum systems. In contrast, hardware built with quantum bits of information are inherently capable of efficiently finding solutions of quantum many-body problems. While a universal and scalable quantum computer is still beyond the horizon, recent advances in qubit manufacturing and coherent control of synthetic quantum matter are leading to a new generation of intermediate scale quantum hardware. \n \nThe complexity underlying quantum many-body systems closely resembles the one encountered in many problems in the world of information and technology. In both contexts, the complexity stems from a large number of interacting degrees of freedom. A powerful strategy in the latter scenario is machine learning, a subfield of artificial intelligence where large amounts of data are used to extract relevant features and patterns. In particular, artificial neural networks have been demonstrated to be capable of discovering low-dimensional representations of complex objects from high-dimensional dataset, leading to the profound technological revolution we all witness in our daily life. \n \nIn this Thesis, we envision a new paradigm for scientific discovery in quantum physics. On the one hand, we have the essentially unlimited data generated with the increasing amount of highly controllable quantum hardware. On the other hand, we have a set of powerful algorithms that efficiently capture non-trivial correlations from high-dimensional data. Therefore, we fully embrace this data-driven approach to quantum mechanics, and anticipate new exciting possibilities in the field of quantum many-body physics and quantum information science. We revive a powerful stochastic neural network called a restricted Boltzmann machine, which slowly moved out of fashion after playing a central role in the machine learning revolution of the early 2010s. We introduce a neural-network representation of quantum states based on this generative model. We propose a set of algorithms to reconstruct unknown quantum states from measurement data and numerically demonstrate their potential, with important implications for current experiments. These include the reconstruction of experimentally inaccessible properties, such as entanglement, and diagnostics to determine sources of noise. Furthermore, we introduce a machine learning framework for quantum error correction, where a neural network learns the best decoding strategy directly from data. We expect that the full integration between quantum hardware and artificial intelligence will become the gold standard, and will drive the world into the era of fault-tolerant quantum computing and large-scale quantum simulations.
Quantum computing establishes a completely new method for performing calculations, which relies on three fundamental principles called superposition, entanglement, and probabilistic measurement. The application of artificial intelligence (AI) with quantum computing provides the ability to develop quantum AI (QAI) agents, which function as intelligent systems using quantum-based methods for representation, optimization, and probabilistic modeling. The research study analyzed the mathematical foundations of quantum computing, together with its key quantum algorithms used in AI and the architectural designs of hybrid quantum–classical agents, alongside quantum learning models and the complete implementation framework, which includes hardware systems, programming methods, and scalability challenges. The study examined different essential obstacles, which included decoherence, data encoding bottlenecks, gradient instability, benchmarking methods, and the comparison between theoretical and practical quantum advantage. Current quantum computing hardware exists within the noisy intermediate-scale quantum computing stage, which offers scientists various ways to investigate hybrid models and variational methods. Scalable and fault-tolerant quantum computing development will enable businesses to achieve groundbreaking results across various fields, including optimization, probabilistic reasoning, and intricate decision-making. The field of AI needs hardware engineering, algorithm advancement, error correction, and assessment methodology development before it can achieve practical quantum advantage. QAI represents an emerging interdisciplinary research frontier that integrates physics, computer science, mathematics, and ethics into a coherent research agenda.
Ediverso Theory: A Unified Theory of Possibility, Choice and Reality Ediverso Theory proposes a new mathematical and conceptual framework for describing how realities emerge from fields of admissible possibilities. While classical mathematics, logic and physics primarily study realised states, determined values and observed outcomes, Ediverso Theory introduces possibility itself as a fundamental object of formal investigation. The framework is built upon four primitive concepts: Possibility (E) — the field of admissible states; Choice (Ω) — the operator that transforms possibility into reality; Memory (H) — the accumulated history of realised states; Coherence (C) — the structure that constrains consistent evolution. From these primitives emerges a unified formal architecture spanning geometry, topology, information theory, observer theory, computational systems, physics, logic, mathematics, consciousness, evolution and collective dynamics. At the core of the theory lies the Ediverso operator: ╠╬╣ which represents a structured field of admissible possibilities rather than a single realised value. Classical mathematics appears as a limiting case when the possibility field collapses to a single point. The work develops: • A Geometry of Possibility• A Topology of Admissible Realities• An Information Theory of Choice• A Formal Theory of Observers• Computational Ediverso Systems for Artificial Intelligence• Possibility-Based Physical Models• Ediverso Logic and Mathematical Foundations• Theories of Consciousness, Evolution and Civilizational Dynamics• The Irreducibility Theorem of Ediverso Systems The central question motivating the entire research programme is: What exists before reality becomes reality? Rather than treating possibility as ignorance, uncertainty or incomplete information, Ediverso Theory investigates the hypothesis that possibility possesses its own independent mathematical structure and that reality emerges through admissible selection within that structure. This manuscript presents the first complete formulation of the Ediverso Research Program and is intended as an open scientific framework subject to mathematical analysis, criticism, refinement, extension and falsification. If successful, Ediverso Theory may provide a common language for describing decision, emergence, information, observation and reality across disciplines traditionally studied in isolation. Reality is not the beginning. Reality is the consequence. Possibility is the beginning. Keywords: Ediverso Theory, Possibility Mathematics, Choice Theory, Emergence, Information Theory, Observer Theory, Mathematical Foundations, Logic, Artificial Intelligence, Consciousness Studies, Complexity Science, Theoretical Physics, Philosophy of Mathematics, Foundations of Reality.