This chapter discusses several previously published results on possible applications of quantum information theory to neuroscience and will present a number of open questions that deserve further study. Applying the quantum information theory of consciousness to modeling the electric processes that occur in neuronal networks in the brain is an open scientific problem that deserves further study. The quantum behavior of individual ion channels is clearly manifested in the discrete changes observed in their channel conductance. Neurons communicate with each other at synaptic contacts that permit the transmission of electric or chemical signals from one neuron to another. Electrical synapses provide a direct electrical coupling between the two neurons through gap junctions that are nanopores constructed from connexin proteins. Multiple protein subunits, each possessing its own geometric shape, may further bind into protein complexes with a quaternary structure. Voltage-gated ion channels provide a typical example of protein complexes that are composed of multiple protein subunits.
This chapter discusses several previously published results on possible applications of quantum information theory to neuroscience and will present a number of open questions that deserve further study. Applying the quantum information theory of consciousness to modeling the electric processes that occur in neuronal networks in the brain is an open scientific problem that deserves further study. The quantum behavior of individual ion channels is clearly manifested in the discrete changes observed in their channel conductance. Neurons communicate with each other at synaptic contacts that permit the transmission of electric or chemical signals from one neuron to another. Electrical synapses provide a direct electrical coupling between the two neurons through gap junctions that are nanopores constructed from connexin proteins. Multiple protein subunits, each possessing its own geometric shape, may further bind into protein complexes with a quaternary structure. Voltage-gated ion channels provide a typical example of protein complexes that are composed of multiple protein subunits.
This research investigates the potential of utilizing quantum entanglement to simulate information encoding and decoding mechanisms within biological neural networks. The core premise is that quantum phenomena, specifically entanglement, may play a more significant role in biological neuronal processing than previously acknowledged. This work proposes a novel approach employing quantum bits (qubits) to represent individual neurons, with connections between neurons established through quantum entanglement. The simulation aims to mimic the processes of synaptic transmission and signal propagation observed in biological neural networks. Mathematical formulations describe the evolution of the entangled qubit states under influence of simulated neuronal inputs, and the measurement processes are modeled to represent the observed output signals. This research offers a theoretical framework for exploring the interplay between quantum mechanics and biological neural systems, potentially leading to a deeper understanding of the fundamental principles governing brain function. The study utilizes a discrete-time simulation approach, allowing for a detailed analysis of the dynamics of the entangled neural network. Key performance indicators, such as the fidelity of information transfer and the robustness of the simulated network, are explored. The results suggest that quantum entanglement can provide a viable model for simulating complex neural processes, warranting further investigation into the potential for quantum computing to revolutionize our understanding of the brain.
Quantum neurobiology is concerned with potential quantum effects operating in the brain and the application of quantum information science to neuroscience problems, the latter of which is the main focus of the current paper. The human brain is fundamentally a multiscalar problem, with complex behavior spanning nine orders of magnitude-scale tiers from the atomic and cellular level to brain networks and the central nervous system. In this review, we discuss a new generation of bio-inspired quantum technologies in the emerging field of quantum neurobiology and present a novel physics-inspired theory of neural signaling (AdS/Brain (anti-de Sitter space)). Three tiers of quantum information science-directed neurobiology applications can be identified. First are those that interpret empirical data from neural imaging modalities (EEG, MRI, CT, PET scans), protein folding, and genomics with wavefunctions and quantum machine learning. Second are those that develop neural dynamics as a broad approach to quantum neurobiology, consisting of superpositioned data modeling evaluated with quantum probability, neural field theories, filamentary signaling, and quantum nanoscience. Third is neuroscience physics interpretations of foundational physics findings in the context of neurobiology. The benefit of this work is the possibility of an improved understanding of the resolution of neuropathologies such as Alzheimer’s disease.
With the development of quantum mechanics, it is applied to different fields, including biology. As intricate as human brains, quantum physics is replacing classical physics in explaining consciousness-related brain functions. The bilayer phospholipid membrane enables neurons in the brain to store and protect quantum information, and the abundance of 1/2-spin phosphorous creates potential for quantum entanglement that allows information to transfer along long distances and process consciousness. Scientists have used Schrödinger's cat thought experiment to explain how the uncertain and superimposed states in quantum physics can be applied to our decision-making behavior with conditions of "Yes" or "No." Scientists also conducted experiments to witness the quantum entanglement of particles in the brain. The observation of the phenomenon broke the pre-assumption that quantum entanglement is too fragile to occur in the chaotic environment in human brains, and it allows the possibility of ongoing conscious processing there. To further understand the decision-making mechanism, physicists should also integrate the knowledge in neuroscience, psychology, sociology, and other interdisciplinary subjects.
Understanding neural excitability and action potential generation is a cornerstone of neuroscience, relying on the complex interplay of biophysical mechanisms governed by ion channels in neuronal membranes. Traditional approaches, such as the Hodgkin-Huxley model, have provided a robust framework for simulating these processes using classical computational techniques. However, the advent of quantum computing opens new avenues for exploring neuronal dynamics, particularly due to ion movement's inherently quantum mechanical properties at atomic scales. In this study, I investigated the intersection of quantum computing and membrane biophysics, evaluating how quantum algorithms could revolutionize simulations of ion channel behavior and neuronal signaling. I began by analyzing the limitations of classical methods in handling high-dimensional, nonlinear systems. Next, I introduced key quantum computing principles, including qubits, superposition, and entanglement, and their potential applications in solving molecular dynamics and differential equations relevant to neuronal activity. Despite promising theoretical advantages, practical challenges such as qubit coherence, error rates, and hardware scalability must be addressed before quantum models can outperform classical simulations. I also discussed hybrid classical-quantum approaches as a transitional strategy, leveraging the strengths of both paradigms. By bridging neuroscience, biophysics, and quantum information science, I aimed to stimulate interdisciplinary research toward more efficient and accurate models of neural excitability.
The quantum theory of mind allows a shift from the Mind/Brain metaphysical problem to the Quantum Mind/Classical Brain scientific problem: how could systematic and coherent quantum processes - assumed to be the physical support of our conscious experiences - occur in a macroscopic system as the brain? I discuss a solution based on a neurobiological model that attributes to quantum computation in intra-neuronal signal-transduction pathways the role of directly supporting phenomenal experience. In this model, quantum coherence is created or prepared by classical mechanisms as recurrent neuronal networks, oscillatory synchrony and gated membrane channels, thus avoiding common theoretical constraints for the existence of quantum communication and computation, as ultra-cold temperatures and quasi-isolation from the environment.
This perspective explores various quantum models of consciousness from the viewpoint of quantum information science, offering potential ideas and insights. The models under consideration can be categorized into three distinct groups based on the level at which quantum mechanics might operate within the brain: those suggesting that consciousness arises from electron delocalization within microtubules inside neurons, those proposing it emerges from the electromagnetic field surrounding the entire neural network, and those positing it originates from the interactions between individual neurons governed by neurotransmitter molecules. Our focus is particularly on the Posner model of cognition, for which we provide preliminary calculations on the preservation of entanglement of phosphate molecules within the geometric structure of Posner clusters. These findings provide valuable insights into how quantum information theory can enhance our understanding of brain functions.