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.
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.
Artificial intelligence (AI) has drawn significant inspiration from neuroscience to develop artificial neural network (ANN) models. However, these models remain constrained by the Von Neumann architecture and struggle to capture the complexity of the biological brain. Quantum computing, with its foundational principles of superposition, entanglement, and unitary evolution, offers a promising alternative approach to modeling neural dynamics. This paper explores the possibility of a neuro-quantum model of the brain by introducing a stochastic quantum approach that incorporates random fluctuations of neuronal processing within a quantum framework. We propose a mathematical formalization of stochastic quantum neural networks (QNNS), where qubits evolve according to stochastic differential equations inspired by biological neuronal processes. We also discuss challenges related to decoherence, qubit stability, and implications for AI and computational neuroscience.
The nervous system is a non-linear dynamical complex system with many feedback loops. A conventional wisdom is that in the brain the quantum fluctuations are self-averaging and thus functionally negligible. However, this intuition might be misleading in the case of non-linear complex systems. Because of an extreme sensitivity to initial conditions, in complex systems the microscopic fluctuations may be amplified and thereby affect the system's behavior. In this way quantum dynamics might influence neuronal computations. Accumulating evidence in non-neuronal systems indicates that biological evolution is able to exploit quantum stochasticity. The recent rise of quantum biology as an emerging field at the border between quantum physics and the life sciences suggests that quantum events could play a non-trivial role also in neuronal cells. Direct experimental evidence for this is still missing but future research should address the possibility that quantum events contribute to an extremely high complexity, variability and computational power of neuronal dynamics.
As the emerging field of quantum biology matures it is becoming possible to understand the fundamental workings of biological systems at the subatomic level. Understanding the complex function of the brain from this vantage point is no exception. Looking at the subcellular functions of neurons beyond the standard classical mechanisms of chemical and electrical signaling offers the potential to design advanced diagnostics and therapeutics for neurological disorders and mental illnesses based on quantum technologies and could even lead to a more comprehensive understanding of human cognition, behavior, or even consciousness itself. Towards this end there have been many theories proposed for how quantum mechanics may play a role in brain function. However, regardless of how promising these theories are, without experimental evidence to provide validation they are merely interesting conjectures. Here we present a review of current theoretical quantum mechanisms proposed to influence brain function (e.g., spin-based mechanisms, optical mechanisms) and place them in the context of existing experimental evidence. We find that experimental evidence does support quantum mechanisms that may exist in the brain, but significant challenges remain in explaining how such mechanisms may influence processes on relevant biological lengths and timescales. Potential approaches to address these challenges are also discussed.
The tantalizing hypothesis that quantum phenomena underpin the brain’s remarkable computational abilities has sparked intense interdisciplinary interest. This study delves into quantum neuroscience by computationally exploring quantum coherence in neural systems, aiming to uncover whether quantum effects enhance information processing. We developed a sophisticated model simulating a 1,000-qubit neural network, with each qubit representing a neuron entangled under biologically relevant conditions (310 K, 0.15 µs coherence time). Using IBM’s Qiskit framework, we tested signal propagation efficiency, latency, and coherence duration across four conditions: quantum models with full, moderate, and high decoherence, and a classical benchmark. Our results reveal a striking 19.4% improvement in signal propagation efficiency in the full-coherence quantum model (95.8% ± 2.9%) compared to the classical model (76.4% ± 5.1%; p < 0.001). Latency was reduced by 31%, with the quantum model achieving 0.68 µs versus 0.98 µs for the classical model. Coherence persisted for up to 1.5 µs, sufficient for short-range neural signaling. Extensive sensitivity analyses, varying temperature (300–325 K), noise (0.01–0.12 µs^-1), and network size (500–1,500 qubits), confirmed robustness, with efficiency remaining above 90% under moderate perturbations. These findings suggest quantum coherence could complement classical neural mechanisms, potentially enhancing processes like sensory integration or consciousness. However, biological complexity, including biochemical interactions, warrants further exploration. We advocate for experimental validation using advanced quantum sensors, such as nitrogen-vacancy centers, to detect coherence in neural tissue. This study bridges quantum physics and neuroscience, offering a robust computational framework to probe the brain’s quantum potential and inspiring future interdisciplinary research into cognition’s mechanistic underpinnings.
Quantum systems exhibit remarkable complexity, often displaying behaviors that defy classical intuition. Controlled chaos, a state where seemingly random fluctuations are linked by intricate feedback loops, is a particularly fascinating area of study. This paper explores the design of a self-organizing quantum system with the potential to generate controlled chaos, aiming to provide a novel approach to understanding and manipulating complex dynamics in quantum mechanics. We propose a system based on a dynamic feedback loop that initiates instability and fosters the emergence of ordered patterns. The core mechanism involves meticulously controlling the initial state to drive a persistent, self-sustaining feedback process. The novelty of this work lies in the integration of quantum mechanics and chaos theory, leveraging the inherent sensitivity of quantum systems to initial conditions and feedback loops to produce demonstrable controlled chaos. This research represents a significant step towards utilizing quantum phenomena to explore and potentially control complex, unpredictable behavior, offering a pathway to deeper insights into the fundamental nature of chaos.