The resting membrane voltage of excitable cells such as neurons and muscle cells is determined by the electrochemical equilibrium of potassium and sodium ions. This voltage is calculated by using the Goldman–Hodgkin–Katz equation. However, from the quantum perspective, ions with significant quantum tunneling through closed channels can interfere with the electrochemical equilibrium and affect the value of the membrane voltage. Hence, in this case the equilibrium becomes quantum electrochemical. Therefore, the model of quantum tunneling of ions is used in this study to modify the Goldman–Hodgkin–Katz equation in such a way to calculate the resting membrane voltage at the point of equilibrium. According to the present calculations, it is found that lithium—with its lower mass—shows a significant depolarizing shift in membrane voltage. In addition to this, when the free gating energy of the closed channels decreases, even sodium and potassium ions depolarize the resting membrane voltage via quantum tunneling. This study proposes the concept of quantum electrochemical equilibrium, at which the electrical potential gradient, the concentration gradient and the quantum gradient (due to quantum tunneling) are balanced. Additionally, this concept may be used to solve many issues and problems in which the quantum behavior becomes more influential.
The resting membrane voltage of excitable cells such as neurons and muscle cells is determined by the electrochemical equilibrium of potassium and sodium ions. This voltage is calculated by using the Goldman–Hodgkin–Katz equation. However, from the quantum perspective, ions with significant quantum tunneling through closed channels can interfere with the electrochemical equilibrium and affect the value of the membrane voltage. Hence, in this case the equilibrium becomes quantum electrochemical. Therefore, the model of quantum tunneling of ions is used in this study to modify the Goldman–Hodgkin–Katz equation in such a way to calculate the resting membrane voltage at the point of equilibrium. According to the present calculations, it is found that lithium—with its lower mass—shows a significant depolarizing shift in membrane voltage. In addition to this, when the free gating energy of the closed channels decreases, even sodium and potassium ions depolarize the resting membrane voltage via quantum tunneling. This study proposes the concept of quantum electrochemical equilibrium, at which the electrical potential gradient, the concentration gradient and the quantum gradient (due to quantum tunneling) are balanced. Additionally, this concept may be used to solve many issues and problems in which the quantum behavior becomes more influential.
It is known that the opening or closing mechanism of a voltage-gated ion channel is triggered by the potential difference across the cell membrane in the nervous system. Based on this picture, we model the ion channel as a nanoscale two-terminal ionic tunneling junction. We apply an external time-varying voltage on the junction to mimic the membrane potential difference in the stimulation of neurons. We derive the non-Markovian quantum Langevin equation from the first principle of quantum mechanics for the ion transport in ion channels, and obtain the ion transport current in terms of quantum tunnelings of ions controlled by the time-varying voltage. We find that the time-varying voltage induces an effective magnetic flux, which causes quantum coherence in ion tunnelings. This effective magnetic flux induces further an oscillatory component to the spectral structure of the ion system, forming a time-dependent quantum memory. Such voltage-induced memory is defined as the active quantum memory, which manifests in the system with a regular oscillatory sideband structure in the ion transport current. The sideband structure demonstrates a multicrossing hysteresis in the current-voltage ( I − V ) curve, responding to the variation of the time-varying voltage (membrane potential difference). We also find that the strength of active quantum memory can be described by the ratio of amplitude and frequency of the time-varying voltage, and can be quantitatively measured through the number of nonzero cross points in the I − V hysteresis and conductance-voltage diagram. Additionally, we explore the temperature dependence of the active quantum memory in such a system. Further, we apply this model to the ion channel system on the biological energy scale. The description of these active quantum memory characteristics provides a quantum mechanical understanding to the underlying mechanism of ion channel dynamics.
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.
We investigate the effects of a quantum entanglement regime within an ion conducting molecule (ion channel) of the neuronal plasma membrane on the onset dynamics of propagating nerve pulses (action potentials). In particular, we model the onset parameters of the sodium current in the Hodgkin Huxley equation as three similar but independent probabilistic mechanisms which become quantum entangled. The underlying physics is general and can involve entanglement between various degrees of freedom underlaying ion transition states or 'gating states' during conduction, e.g. Na+ ions in different channel locations, or different 'affinity' states of ions with atoms lining the sub-regions of the channel protein ('filter-states'). We find that the 'quantum corrected' Hodgkin Huxley equation incorporating entangled systems states can reproduce action potential pulses with the critical onset dynamics observed recently in neocortical neurons in vivo by Naundorf et al. [Nature {\bf 440}, 1060 (20 April 2006)]. Interestingly, the suggested entanglement term can also slow down action potential initiation.
Sodium-ion batteries (SIBs) are becoming progressively more prevalent as a feasible substitute for lithium-ion batteries (LIBs) due to the rising need for affordable and sustainable energy storage on a worldwide scale. Because sodium is abundant and relatively inexpensive, SIBs seem appealing. However, the intricacy of their electrochemical properties, which includes ion transport, intercalation kinetics, and thermal characteristics poses a hindrance to their broad adoption. Current available classical algorithms struggle with scalability, nonlinearity, and computational intensity of electrical vehicle (EV) battery models. This paper proposes a quantum-assisted state estimation framework tailored to enhance predictive accuracy and computational robustness of battery state estimation using a hybrid quantum-classical model. Our approach proposes utilizing simulated data derived from a physics-based pseudo-twodimensional (P2D) electrochemical model to train and validate a variational quantum circuit (VQC) designed for state of charge (SOC) and voltage prediction. Although no quantum hardware execution is performed in this study, rather this work introduces a framework and simulation-ready methodology for future deployment. The results show theoretical viability and potential for superior (hybrid) quantum performance compared to classical-only models, laying the groundwork for quantumenhanced battery management systems.
Abstract Recently, it has been suggested that ion channel selectivity filter may exhibit quantum coherence, which may be appropriate to explain ion selection and conduction processes. Potassium channels play a vital role in many physiological processes. One of their main physiological functions is the efficient and highly selective transfer of K + ions through the membranes into the cells. To do this, ion channels must be highly selective, allowing only certain ions to pass through the membrane, while preventing the others. The present research is an attempt to investigate the relationship between hopping rate and maintaining coherence in ion channels. Using the Lindblad equation to describe a three-level system, the results in different quantum regimes are examined. We studied the distillable coherence and the second order coherence function of the system. The oscillation of distillable coherence from zero, after the decoherence time, and also the behavior of the coherence function clearly show the point that the system is coherent in ion channels with high throughput rates.
While the electric field in synthetic ion-exchange membranes has been found to be described by the equations of Teorell, and Meyer and Sievers, in the case of isotonic osmosis, Planck–Goldman's potential, first found in the nerve, is important. In contrast to Teorell's equation the latter integral represents a potential function. Beneath this integral with respect to position we have to explore the integral with respect to time as results from nervous excitation. This assumes a minimum value if a potential controls its course. Indeed, this integral, the action, yields Planck's constant.To explain this phenomenon, membranes must be considered as polymers doped with Na and/or K atoms. While this process results in the capture of the valence electrons of the dopants by any traps of the membrane, the interaction of these electrons with the remaining cations continues. In this way, the membrane merely represents the carrier of an atomic process resulting in the diffusion of the cations from trap to trap.According to potential theory we have to regard the K˙ and Na˙ potentials in nervous excitation as stationary while the transition from the first state into the second state will be controlled by the Green function. Applying to the partial differential equation of the action controlling Green's function, Jacobi's method of the complete integral we come to the goal if the minimum value of the variational problem in space and time is determined by using Dirichlett's integral. From this we find that in a doped polymer the friction controlling the ion transport depends only on the electrons of the dopant atoms.Finally, from the experimentally determined values of the frictional coefficients one obtains a value of the action which is agreement with Planck's constant to within 6%. As our calculation is based only on classical methods and no quantum conditions were taken into account this result is surprising.