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arXiv:2509.01021 · Discover Artificial Intelligence

Quantum-like coherence derived from the interaction between chemical reaction and its environment

Yukio-Pegio Gunji, Andrew Adamatzky, Panagiotis Mougkogiannis, Andrei Yu. Khrennikov

Published Jan 6, 2026• 5 citations
arXivHTMLPDF

Abstract

Abstract By uncovering the contrast between Artificial Intelligence and Natural-born Intelligence as a computational process, we define closed computing and open computing, and implement open computing within chemical reactions. This involves forming a mixture and invalidation of the computational process and the execution environment, which are logically distinct, and coalescing both to create a system that adjusts fluctuations. We model chemical reactions by considering the computation as the chemical reaction and the execution environment as the degree of aggregation of molecules that interact with the reactive environment. This results in a chemical reaction that progresses while repeatedly clustering and de-clustering, where concentration no longer holds significant meaning.Open computing is segmented into Token computing, which focuses on the individual behavior of chemical molecules, and Type computing, which focuses on normative behavior. Ultimately, both are constructed as an interplay between the two. In this system, Token computing demonstrates self-organizing critical phenomena, while Type computing exhibits quantum logic. Through their interplay, the recruitment of fluctuations is realized, giving rise to interactions between quantum logical subspaces corresponding to quantum coherence across different Hilbert spaces. As a result, spike waves are formed, enabling signal transmission. This occurrence may be termed quantum-like coherence, implying the source of enzymes responsible for controlling spike waves and biochemical rhythms.

DOI
10.1007/s44163-025-00773-0

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Abstract By uncovering the contrast between Artificial Intelligence and Natural-born Intelligence as a computational process, we define closed computing and open computing, and implement open computing within chemical reactions. This involves forming a mixture and invalidation of the computational process and the execution environment, which are logically distinct, and coalescing both to create a system that adjusts fluctuations. We model chemical reactions by considering the computation as the chemical reaction and the execution environment as the degree of aggregation of molecules that interact with the reactive environment. This results in a chemical reaction that progresses while repeatedly clustering and de-clustering, where concentration no longer holds significant meaning.Open computing is segmented into Token computing, which focuses on the individual behavior of chemical molecules, and Type computing, which focuses on normative behavior. Ultimately, both are constructed as an interplay between the two. In this system, Token computing demonstrates self-organizing critical phenomena, while Type computing exhibits quantum logic. Through their interplay, the recruitment of fluctuations is realized, giving rise to interactions between quantum logical subspaces corresponding to quantum coherence across different Hilbert spaces. As a result, spike waves are formed, enabling signal transmission. This occurrence may be termed quantum-like coherence, implying the source of enzymes responsible for controlling spike waves and biochemical rhythms.
Modeling chemical dynamics is essential for understanding, predicting, and optimizing chemical processes. Because realistic chemical systems involve many interacting degrees of freedom, direct propagation of the many-body Schr"{o}dinger equation is intractable for all but the smallest systems. Quantum information platforms offer a fundamentally different representation: a ddd-dimensional state may be encoded using k=log2(d)k=log_2(d)k=log2​(d) qubits rather than storing each amplitude independently. To harness this advantage for chemistry, it is necessary to develop quantum algorithms and hardware architectures capable of simulating the open-system dynamics ubiquitous in condensed-phase and molecular environments. In the first chapter, we develop a theoretical framework for simulating a cavity-modified photoinduced charge-transfer reaction in the carotenoid–porphyrin–C60_{60}60​ (CPC60_{60}60​) molecular triad dissolved in tetrahydrofuran (THF). Nonequilibrium Fermi’s Golden Rule (NE-FGR) rate coefficients are obtained as a product of cavity-free and cavity-modified correlation functions. We further introduce an approximation based on Instantaneous Marcus Theory (IMT) that closely tracks the full NE-FGR result across all relevant time scales. Population transfer rates computed from the population-only off-diagonal quantum master equation (p-tcl-OD-QME) are shown to be consistent with the NE-FGR predictions. Our results demonstrate that nonequilibrium charge-transfer dynamics are significantly modified by coupling to an optical cavity. The second chapter addresses the integration of linear nonautonomous ordinary differential equations on digital quantum hardware. Rather than classically constructing a nonunitary propagator at each time step and embedding it into a larger unitary operation for execution on quantum hardware, we eliminate this classical–quantum partitioning. We derive and propagate differential equations for the singular value decomposition (SVD) factors of the propagator directly, enabling time evolution to be implemented within a quantum-compatible framework. This approach shifts the locus of time integration from the classical processor to the quantum device. In the third chapter, we investigate the feasibility of simulating chemical reaction dynamics on a parametrically driven bosonic superconducting Kerr-cat device. This analog platform enables direct control over parameters defining an effective double-well free-energy landscape, as well as over environmental factors such as temperature and system–bath coupling strength. We demonstrate that proton transfer dynamics in prototypical benchmark systems, including hydrogen-bonded dimers of malonaldehyde and DNA base pairs, can be accurately reproduced using experimentally accessible Kerr-cat architectures.
This paper explores the potential of quantum computing for simulating complex chemical reactions. Traditional classical computational methods often struggle with the inherent quantum mechanical nature of chemical systems, particularly when dealing with large molecules and intricate reaction pathways. This work proposes a novel quantum algorithm leveraging superposition and entanglement to directly simulate the time evolution of these systems. The core of the algorithm centers on a time-dependent Schrödinger equation (TDSE) implemented on a quantum computer. We outline the key elements, including the encoding of molecular states, the construction of a suitable Hamiltonian operator, and the execution of the quantum algorithm to obtain the final state of the system. The efficiency gains compared to classical methods, particularly for reactions involving significant quantum effects, are discussed. Furthermore, we highlight the potential impact of this technology on accelerating drug discovery, materials science, and fundamental chemical research. The algorithm's core relies on the ability of a quantum computer to perform a vast number of calculations simultaneously, offering a substantial advantage over classical computers when simulating quantum systems.
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In recent years, the modeling interest has increased significantly from the molecular level to the atomic and quantum scale. The field of computational chemistry plays a significant role in designing computational models for the operation and simulation of systems ranging from atoms and molecules to industrial-scale processes. It is influenced by a tremendous increase in computing power and the efficiency of algorithms. The representation of chemical reactions using classical automata theory in thermodynamic terms had a great influence on computer science. The study of chemical information processing with quantum computational models is a natural goal. In this paper, we have modeled chemical reactions using two-way quantum finite automata, which are halted in linear time. Additionally, classical pushdown automata can be designed for such chemical reactions with multiple stacks. It has been proven that computational versatility can be increased by combining chemical accept/reject signatures and quantum automata models.
Precise prediction and control of chemical reactions and material propertiesthe central goal of precision chemistrytruly rely on accurate and efficient theoretical modeling of chemical systems. In recent years, significant progress has been made in theoretical and computational chemistry. Especially, interdisciplinary studies in the direction of the two emerging computational frontiers, artificial intelligence and quantum computation, are revolutionizing this field. Expressive neural networks can be used to represent many-electron wave functions with high accuracy, capturing strong correlations with a favorable computational cost compared to other methods. Leveraging quantum computation in quantum chemistry is also expected to deliver a quantum advantage in problems including electronic structure calculations. With artificial intelligence and quantum computation, we expect to establish a unified, high-precision, and scalable computational foundation for generating accurate quantum chemical data essential for precision chemistry.
The highly selective Na3 + Br reaction creates a continuous electronic population inversion on Na2 transitions which demonstrates optical gain through stimulated emission in regions close to 527, 492, and 460 nm ((alpha) equals 8 X 10-3 cm-1 for an individual rotational level at approximately 527 nm). With a focus to increasing amplifier gain length and amplifying medium concentration (conversion to oscillator), we extend these initial studies employing the intersection of sodium and bromine flows formed from slit source expansions to create an extended reaction-amplification zone. Initial results with this extended reaction zone provide the first example of chemically induced Raman pumping. Unique Raman signals are induced by and correlate with emission from the NaD-line components (D1, D2) formed in the chemical reaction zone primarily as a result of the Na2 + Br yields Na* (3P) + NaBr reaction. The NaD-line emitters interact with cooled sodium dimers in a resonance Raman scattering process, for which computer simulation of the experimentally observed spectra predict an unusually large scattering linewidth close to 4 cm-1. We describe initial laser based studies on a pure sodium expansion to probe the mechanism of the unusually strong Raman scattering and to understand the origin of the large scattering linewidth. These experiments demonstrate that the Raman process can be induced only upon excitation of the NaD-line components, the Raman scattering being insufficient or nonexistent for wavelength shifts (Delta) (lambda) > 1 nm from the D lines. The scattering linewidth for the reactive process is not readily correlated with the line-broadening of the NaD-line and may result from the nature of the Na- Na2 interaction in the reactive environment. Initial double pass measurements indicate the possibility of optical gain associated with the Raman process.