This manuscript proposes a testable research framework for studying two-phase Bayesian inference in open, noisy, brain-like systems. The work does not assume the existence of macroscopic quantum computation in biological tissue. Instead, it investigates whether resonance-assisted dynamics in dissipative systems can produce quantum-like computational advantages without long-lived coherence. The framework integrates variational Bayesian inference, stochastic resonance, and nonlinear feedback mechanisms. Concrete experimental and theoretical pathways are outlined to validate or falsify the proposed mechanisms. This project is now under legal monitoring. All commercial implementations must contact the author.
This manuscript proposes a testable research framework for studying two-phase Bayesian inference in open, noisy, brain-like systems. The work does not assume the existence of macroscopic quantum computation in biological tissue. Instead, it investigates whether resonance-assisted dynamics in dissipative systems can produce quantum-like computational advantages without long-lived coherence. The framework integrates variational Bayesian inference, stochastic resonance, and nonlinear feedback mechanisms. Concrete experimental and theoretical pathways are outlined to validate or falsify the proposed mechanisms. This project is now under legal monitoring. All commercial implementations must contact the author.
Quantum-enhanced Bayesian inference offers the potential to significantly accelerate probabilistic predictions compared to classical Bayesian methods, particularly in complex Bayesian networks. This paper explores the implementation of a novel quantum-enhanced Bayesian inference algorithm, utilizing quantum computation to optimize Bayesian updates and inference. We present a framework for leveraging quantum hardware to enhance the accuracy and efficiency of probabilistic predictions within Bayesian network modeling. The core mechanism focuses on quantum-assisted Bayesian updates, designed to overcome computational bottlenecks inherent in traditional Bayesian inference. The proposed algorithm demonstrates improved accuracy and scalability through a rigorous analysis of its performance on a representative set of Bayesian network scenarios. The investigation delves into the potential advantages and challenges of integrating quantum computing into the Bayesian inference process.
This manuscript aims to illustrate a quantum-classical dissipative theory (suited to be converted to effective algorithms for numerical simulations) within the long-term project of studying molecular processes in the brain. Other approaches, briefly sketched in the text, have advocated the need to deal with both quantum and classical dynamic variables when studying the brain. At variance with these other frameworks, the manuscript’s formalism allows us to explicitly treat the classical dynamical variables. The theory must be dissipative not because of formal requirements but because brain processes appear to be dissipative at the molecular, physiological, and high functional levels. We discuss theoretically that using Brownian dynamics or the Nosè-Hoover-Chain thermostat to perform computer simulations provides an effective way to introduce an arrow of time for open quantum systems in a classical environment. In the future, We plan to study classical models of neurons and astrocytes, as well as their networks, coupled to quantum dynamical variables describing, e.g., nuclear and electron spins, HOMO and LUMO orbitals of phenyl and indole rings, ion channels, and tunneling protons.
Quantum-enhanced Bayesian inference represents a significant advancement in statistical modeling, leveraging the unique properties of quantum computing to accelerate probabilistic computations and improve model accuracy. This paper explores the potential of quantum mechanics to revolutionize Bayesian inference, focusing on the implementation of quantum-assisted probability calculations, quantum entanglement and superposition for enhanced data processing, and quantum-enhanced parameter estimation and model prediction. This investigation addresses the limitations of classical Bayesian inference by introducing a novel framework centered on quantum computing. We detail the core mechanism behind this approach, emphasizing the utilization of quantum bits (qubits) to improve the efficiency and accuracy of Bayesian inference. This work presents a substantial contribution to the field of statistical modeling and explores the practical implications of quantum-enhanced Bayesian inference.
This paper proposes a novel Bayesian inference algorithm termed "Quantum-Inspired Bayesian Inference" (QIBI), drawing inspiration from quantum mechanics, particularly superposition and entanglement. Classical Bayesian inference struggles with representing and processing highly complex and uncertain data, often relying on a single, definitive probability distribution. QIBI addresses this limitation by introducing a mechanism analogous to quantum superposition, allowing the algorithm to simultaneously consider multiple hypotheses, each weighted by its "quantum coherence." Quantum coherence represents the degree to which a hypothesis consistently explores the solution space. The algorithm's core equation is: P(Hi | D) = (1/Z) * [Ψ*(D) * P(Hi)] Where: * P(Hi | D) is the posterior probability of hypothesis *Hi* given data *D*. * Ψ(D) is the quantum coherence vector representing the superposition state. * P(Hi) is the prior probability of hypothesis *Hi*. * Z is the normalization constant. The implementation of QIBI offers potential advantages over traditional Bayesian methods, particularly in scenarios with high dimensionality, complex dependencies, and significant uncertainty. This work outlines the theoretical framework, the core equation, and the rationale behind QIBI's approach, suggesting a pathway towards more robust and efficient Bayesian inference by leveraging principles from the quantum world. The algorithm's performance is theoretically assessed, and future research directions are discussed, focusing on practical implementation and comparative analysis with established Bayesian techniques.
The nervous system probably cannot display macroscopic quantum (i.e. classically impossible) behaviours such as quantum entanglement, superposition or tunnelling (Koch and Hepp, Nature 440:611, 2006). However, in contrast to this quantum ‘mysticism’ there is an alternative way in which quantum events might influence the brain activity. The nervous system is a nonlinear system with many feedback loops at every level of its structural hierarchy. A conventional wisdom is that in macroscopic objects the quantum fluctuations are self-averaging and thus not important. Nevertheless this intuition might be misleading in the case of nonlinear complex systems. Because of a high sensitivity to initial conditions, in chaotic systems the microscopic fluctuations may be amplified upward and thereby affect the system's output. In this way stochastic quantum dynamics might sometimes alter the outcome of neuronal computations, not by generating classically impossible solutions, but by influencing the selection of many possible solutions (Satinover, Quantum Brain, Wiley & Sons, 2001). I am going to discuss recent theoretical proposals and experimental findings in quantum mechanics, complexity theory and computational neuroscience suggesting that biological evolution is able to take advantage of quantum-computational speed-up. I predict that the future research on quantum complex systems will provide us with novel interesting insights that might be relevant also for neurobiology and neurophilosophy.
Abstract The nervous system probably cannot display macroscopic quantum (i.e. classically impossible) behaviours such as quantum entanglement, superposition or tunnelling (Koch and Hepp, Nature 440:611, 2006). However, in contrast to this quantum ‘mysticism’ there is an alternative way in which quantum events might influence the brain activity. The nervous system is a nonlinear system with many feedback loops at every level of its structural hierarchy. A conventional wisdom is that in macroscopic objects the quantum fluctuations are self-averaging and thus not important. Nevertheless this intuition might be misleading in the case of nonlinear complex systems. Because of a high sensitivity to initial conditions, in chaotic systems the microscopic fluctuations may be amplified upward and thereby affect the system's output. In this way stochastic quantum dynamics might sometimes alter the outcome of neuronal computations, not by generating classically impossible solutions, but by influencing the selection of many possible solutions (Satinover, Quantum Brain, Wiley & Sons, 2001). I am going to discuss recent theoretical proposals and experimental findings in quantum mechanics, complexity theory and computational neuroscience suggesting that biological evolution is able to take advantage of quantum-computational speed-up. I predict that the future research on quantum complex systems will provide us with novel interesting insights that might be relevant also for neurobiology and neurophilosophy.