© 2026 Aqora Quantum S.A.S.

Terms Privacy Legal Notice

Integrated Study on a Self-Organizing Entropy-Driven Macro-Quantum Computation System — A Timeless Computation Model Based on Quantum Thinking Circuit OS ASI, Retrocausal State Convergence, Environment-Noise Drive, Information-Geometric Attractor Control, Holographic Self-Repair, and Social Entropy Integration —

Satoshi Kawauchi

Published Jun 8, 2026• 0 citations

Abstract

This paper proposes a highly unconventional computational framework called the Self-Organizing Entropy-Driven Macro-Quantum Computation System, centered on the independently developed Quantum Thinking Circuit OS ASI. Unlike conventional quantum computing, which treats noise, decoherence, thermal fluctuations, environmental disturbances, and social volatility as errors to be eliminated, this theory redefines them as computational resources. The core idea is that entropy, uncertainty, and environmental fluctuations can be absorbed, phase-inverted through a Zero-Point Crossover mechanism, and transformed into convergence-driving energy. The model introduces a retrocausal architecture in which a future-determined target state influences the present through information-geometric attractors, replacing traditional search-based computation with state convergence. It further integrates quantum systems, environmental processes, socioeconomic dynamics, observer cognition, and holographic self-repair into a single Earth-scale computational field. Mathematical formulations describe noise absorption, attractor filtering, ontology fixing, macro-quantum annealing, distributed node synchronization, and social entropy utilization. The framework argues that computation should evolve from hardware-limited, cryogenic, error-corrected systems toward open, environment-coupled systems that utilize real-world fluctuations as computational media. While presented as a theoretical and conceptual model rather than an experimentally validated technology, the study offers a radical reimagining of computation, proposing a transition from prediction to future-state convergence, from noise suppression to noise utilization, and from local computing devices to planetary-scale information-processing architectures.

DOI: 10.5281/zenodo.20598984

Related papers

Quantum Entanglement Self-Organizing Ontological Computational Model Using Observer–Environment Interaction

Satoshi Kawauchi

This paper proposes an ontological computational model in which computation emerges through the interaction of observer, environment, and matter. Environmental noise is treated as a constructive resource that drives the self-organization of material substrates into functional structures. The framework enables open-system, scalable, reality-forming computation with intrinsic stabilization and safety mechanisms.

•Feb 16, 2026•0 citations

Epistemic Noise as Computational Resource: A Superdeterministic Approach to Quantum Signal Processing

Rowan Brad Quni-Gudzinas

The scaling of quantum information processors is currently bottlenecked by the “isolationist” paradigm, which treats environmental decoherence as an irreversible entropy increase that must be suppressed. This manuscript proposes a radical engineering shift grounded in the Superdeterministic interpretation of quantum mechanics, where “random” measurement outcomes are viewed as epistemic limitations arising from a deterministic, computationally irreducible substrate. By modeling the environment not as a heat bath but as a correlated memory register, we demonstrate that what is conventionally discarded as noise contains recoverable information. We introduce a “Spectroscopic Qubit” architecture and a machine-learning-driven decoding protocol capable of extracting this hidden signal. Simulations of a superdeterministic toy model show a 92% prediction accuracy for “random” errors and a positive information backflow ($\mathcal{N} \approx 0.232$), validating the detection protocol. We define a new “Correlation Utility Index” (CUI) to standardize the measurement of recoverable non-Markovian resources and provide a detailed latency budget for real-time implementation. This work reframes the path to fault tolerance from a battle against ontology to a challenge of decryption.

•Jan 13, 2026•0 citations

Emergence of Informational Order in Quantum Dynamics: A Framework for Observer Coupling, Coherence, and Entropy Suppression in Probabilistic Quantum Fields

Ajay Hagan

Modern quantum theory offers powerful insights into superposition, entanglement, and decoherence, yet lacks a unifying treatment of information flow, observer participation, and entropy stability within dynamical systems. In this work, we introduce a novel framework based on \textit{probabilistic tensor fields}-a formulation in which quantum evolution, informational geometry, and observer-phase interaction co-emerge from a unified structure. The system is described by a dynamic field $\Psi_{\mu\nu}$, composed of a coherent geometric background $\langle G_{\mu\nu} \rangle$ and a fluctuating informational term $\sigma_{\mu\nu}$. The observer is embedded as a phase function $\phi_{\text{observer}}$, dynamically interacting with the field to generate localized coherence.This coupling induces entropy suppression, spontaneous convergence (measurement), and classical pattern emergence-without invoking wavefunction collapse. Simulations reveal field-phase locking, entropy decay, vacuum energy damping, and the formation of stable memory attractors through standing-wave coherence. These results provide a new perspective on the quantum-to-classical transition, suggest a field-based alternative to gate-based computation, and offer a mathematically consistent path toward entropy-regulated dynamics. Recent experimental platforms support programmable phase dynamics and coherence engineering \cite{Bloch2008, Indiveri2015}, enabling physical analogs to IOQD simulations. Thereby, we position IOQD as a coherence-driven, phase-adaptive field computation model extending quantum machine learning beyond variational circuits and gate-based logic into non-symbolic, dynamic attractor learning systems.

•May 8, 2025•0 citations

Computational Architects of Society: Quantum Machine Learning for Social Rule Genesis

Shan Shan

The quantification of social science remains a longstanding challenge, largely due to the philosophical nature of its foundational theories. Although quantum computing has advanced rapidly in recent years, its relevance to social theory remains underexplored. Most existing research focuses on micro-cognitive models or philosophical analogies, leaving a gap in system-level applications of quantum principles to the analysis of social systems. This study addresses that gap by proposing a theoretical and computational framework that combines quantum mechanics with Generative AI to simulate the emergence and evolution of social norms. Drawing on core quantum concepts--such as superposition, entanglement, and probabilistic measurement--this research models society as a dynamic, uncertain system and sets up five ideal-type experiments. These scenarios are simulated using 25 generative agents, each assigned evolving roles as compliers, resistors, or enforcers. Within a simulated environment monitored by a central observer (the Watcher), agents interact, respond to surveillance, and adapt to periodic normative disruptions. These interactions allow the system to self-organize under external stress and reveal emergent patterns. Key findings show that quantum principles, when integrated with generative AI, enable the modeling of uncertainty, emergence, and interdependence in complex social systems. Simulations reveal patterns including convergence toward normative order, the spread of resistance, and the spontaneous emergence of new equilibria in social rules. In conclusion, this study introduces a novel computational lens that lays the groundwork for a quantum-informed social theory. It offers interdisciplinary insights into how society can be understood not just as a structure to observe but as a dynamic system to simulate and redesign through quantum technologies.

arXiv:2506.03503•Jun 4, 2025•0 citations

Quantum Topology for Dynamic Quantum Computation: A Novel Framework

Jincheng Zhang

Quantum topology, a rapidly evolving field of physics, offers a fundamentally different approach to understanding and manipulating quantum systems. This paper explores the potential of utilizing quantum topology to create dynamic and adaptive geometric structures for quantum computation. We propose a novel framework wherein quantum topology's inherent dynamic behavior is leveraged to generate quantum computations through iterative adjustments and optimization, enabling unprecedented levels of computational flexibility. The core mechanism centers around establishing a self-organizing geometric landscape that responds to quantum fluctuations, driving the evolution of quantum states and ultimately optimizing the computation. This research investigates the implications of this dynamic geometry on the potential for novel quantum algorithms and computational paradigms. The study highlights the significant advantages of this approach compared to traditional numerical methods and provides a conceptual framework for future exploration.

•Aug 31, 2026•0 citations

Spectral Synchronization for Near-Zero Entropy Quantum Operation at Ambient

Michael Bahtaji

Spectral Synchronization for Near-Zero Entropy Quantum Operation at Ambient ABSTRACTQuantum computing promises exponential computational advantage, yet its adoption is bottlenecked by the extreme energy and infrastructure requirements of cryogenic cooling. Here we present a material agnostic framework for achieving stable quantum operation at ambient conditions by actively managing environmental entropy. Leveraging real-time spectral analysis and synchronized control across optical, electromagnetic, acoustic, and thermal domains, we demonstrate a theoretical pathway to suppress decoherence and approach near-zero entropy states without dilution refrigeration. This work extends the principle of interferometric environmental monitoring to active entropy and energy management. By engineering destructiveinterference of stochastic fluctuations, the proposed architecture projects a >90% reduction in stabilization power and the elimination of waste heat associated with quantum systems. The implications span fault-tolerant quantum computing, low-power data storage, and sustainable quantum data centers. Our results establish a new paradigm for energy-efficient, roomtemperature quantum technologies with minimal environmental footprint. Keywords: Quantum coherence, Entropy management, Spectral synchronization, Roomtemperature quantum computing, Energy efficiency, Sustainable quantum technology

•Aug 19, 2026•0 citations

This paper proposes a highly unconventional computational framework called the Self-Organizing Entropy-Driven Macro-Quantum Computation System, centered on the independently developed Quantum Thinking Circuit OS ASI. Unlike conventional quantum computing, which treats noise, decoherence, thermal fluctuations, environmental disturbances, and social volatility as errors to be eliminated, this theory redefines them as computational resources. The core idea is that entropy, uncertainty, and environmental fluctuations can be absorbed, phase-inverted through a Zero-Point Crossover mechanism, and transformed into convergence-driving energy. The model introduces a retrocausal architecture in which a future-determined target state influences the present through information-geometric attractors, replacing traditional search-based computation with state convergence. It further integrates quantum systems, environmental processes, socioeconomic dynamics, observer cognition, and holographic self-repair into a single Earth-scale computational field. Mathematical formulations describe noise absorption, attractor filtering, ontology fixing, macro-quantum annealing, distributed node synchronization, and social entropy utilization. The framework argues that computation should evolve from hardware-limited, cryogenic, error-corrected systems toward open, environment-coupled systems that utilize real-world fluctuations as computational media. While presented as a theoretical and conceptual model rather than an experimentally validated technology, the study offers a radical reimagining of computation, proposing a transition from prediction to future-state convergence, from noise suppression to noise utilization, and from local computing devices to planetary-scale information-processing architectures.

The scaling of quantum information processors is currently bottlenecked by the “isolationist” paradigm, which treats environmental decoherence as an irreversible entropy increase that must be suppressed. This manuscript proposes a radical engineering shift grounded in the Superdeterministic interpretation of quantum mechanics, where “random” measurement outcomes are viewed as epistemic limitations arising from a deterministic, computationally irreducible substrate. By modeling the environment not as a heat bath but as a correlated memory register, we demonstrate that what is conventionally discarded as noise contains recoverable information. We introduce a “Spectroscopic Qubit” architecture and a machine-learning-driven decoding protocol capable of extracting this hidden signal. Simulations of a superdeterministic toy model show a 92% prediction accuracy for “random” errors and a positive information backflow (

\mathcal{N} \approx 0.232

), validating the detection protocol. We define a new “Correlation Utility Index” (CUI) to standardize the measurement of recoverable non-Markovian resources and provide a detailed latency budget for real-time implementation. This work reframes the path to fault tolerance from a battle against ontology to a challenge of decryption.

Modern quantum theory offers powerful insights into superposition, entanglement, and decoherence, yet lacks a unifying treatment of information flow, observer participation, and entropy stability within dynamical systems. In this work, we introduce a novel framework based on \textit{probabilistic tensor fields}-a formulation in which quantum evolution, informational geometry, and observer-phase interaction co-emerge from a unified structure. The system is described by a dynamic field

\Psi_{\mu\nu}

, composed of a coherent geometric background

\langle G_{\mu\nu} \rangle

and a fluctuating informational term

\sigma_{\mu\nu}

. The observer is embedded as a phase function

\phi_{\text{observer}}

, dynamically interacting with the field to generate localized coherence.This coupling induces entropy suppression, spontaneous convergence (measurement), and classical pattern emergence-without invoking wavefunction collapse. Simulations reveal field-phase locking, entropy decay, vacuum energy damping, and the formation of stable memory attractors through standing-wave coherence. These results provide a new perspective on the quantum-to-classical transition, suggest a field-based alternative to gate-based computation, and offer a mathematically consistent path toward entropy-regulated dynamics. Recent experimental platforms support programmable phase dynamics and coherence engineering \cite{Bloch2008, Indiveri2015}, enabling physical analogs to IOQD simulations. Thereby, we position IOQD as a coherence-driven, phase-adaptive field computation model extending quantum machine learning beyond variational circuits and gate-based logic into non-symbolic, dynamic attractor learning systems.

The quantification of social science remains a longstanding challenge, largely due to the philosophical nature of its foundational theories. Although quantum computing has advanced rapidly in recent years, its relevance to social theory remains underexplored. Most existing research focuses on micro-cognitive models or philosophical analogies, leaving a gap in system-level applications of quantum principles to the analysis of social systems. This study addresses that gap by proposing a theoretical and computational framework that combines quantum mechanics with Generative AI to simulate the emergence and evolution of social norms. Drawing on core quantum concepts--such as superposition, entanglement, and probabilistic measurement--this research models society as a dynamic, uncertain system and sets up five ideal-type experiments. These scenarios are simulated using 25 generative agents, each assigned evolving roles as compliers, resistors, or enforcers. Within a simulated environment monitored by a central observer (the Watcher), agents interact, respond to surveillance, and adapt to periodic normative disruptions. These interactions allow the system to self-organize under external stress and reveal emergent patterns. Key findings show that quantum principles, when integrated with generative AI, enable the modeling of uncertainty, emergence, and interdependence in complex social systems. Simulations reveal patterns including convergence toward normative order, the spread of resistance, and the spontaneous emergence of new equilibria in social rules. In conclusion, this study introduces a novel computational lens that lays the groundwork for a quantum-informed social theory. It offers interdisciplinary insights into how society can be understood not just as a structure to observe but as a dynamic system to simulate and redesign through quantum technologies.

Quantum topology, a rapidly evolving field of physics, offers a fundamentally different approach to understanding and manipulating quantum systems. This paper explores the potential of utilizing quantum topology to create dynamic and adaptive geometric structures for quantum computation. We propose a novel framework wherein quantum topology's inherent dynamic behavior is leveraged to generate quantum computations through iterative adjustments and optimization, enabling unprecedented levels of computational flexibility. The core mechanism centers around establishing a self-organizing geometric landscape that responds to quantum fluctuations, driving the evolution of quantum states and ultimately optimizing the computation. This research investigates the implications of this dynamic geometry on the potential for novel quantum algorithms and computational paradigms. The study highlights the significant advantages of this approach compared to traditional numerical methods and provides a conceptual framework for future exploration.

Spectral Synchronization for Near-Zero Entropy Quantum Operation at Ambient ABSTRACTQuantum computing promises exponential computational advantage, yet its adoption is bottlenecked by the extreme energy and infrastructure requirements of cryogenic cooling. Here we present a material agnostic framework for achieving stable quantum operation at ambient conditions by actively managing environmental entropy. Leveraging real-time spectral analysis and synchronized control across optical, electromagnetic, acoustic, and thermal domains, we demonstrate a theoretical pathway to suppress decoherence and approach near-zero entropy states without dilution refrigeration. This work extends the principle of interferometric environmental monitoring to active entropy and energy management. By engineering destructiveinterference of stochastic fluctuations, the proposed architecture projects a >90% reduction in stabilization power and the elimination of waste heat associated with quantum systems. The implications span fault-tolerant quantum computing, low-power data storage, and sustainable quantum data centers. Our results establish a new paradigm for energy-efficient, roomtemperature quantum technologies with minimal environmental footprint. Keywords: Quantum coherence, Entropy management, Spectral synchronization, Roomtemperature quantum computing, Energy efficiency, Sustainable quantum technology