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.
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.
"Quantum Computing for the Brain argues that the brain is the killer application for quantum computing. No other system is as complex, as multidimensional in time and space, as dynamic, as less well-understood, as of peak interest, and as in need of three-dimensional modeling as it functions in real-life, as the brain. Quantum computing has emerged as a platform suited to contemporary data processing needs, surpassing classical computing and supercomputing. This book shows how quantum computing's increased capacity to model classical data with quantum states and the ability to run more complex permutations of problems can be employed in neuroscience applications such as neural signaling and synaptic integration. State-of-the-art methods are discussed such as quantum machine learning, tensor networks, Born machines, quantum kernel learning, wavelet transforms, Rydberg atom arrays, ion traps, boson sampling, graph-theoretic models, quantum optical machine learning, neuromorphic architectures, spiking neural networks, quantum teleportation, and quantum walks. Quantum Computing for the Brain is a comprehensive one-stop resource for an improved understanding of the converging research frontiers of foundational physics, information theory, and neuroscience in the context of quantum computing"--
The question of whether quantum mechanical phenomena play a functional role in brain activity and the emergence of consciousness remains one of the most controversial yet intellectually stimulating debates at the intersection of physics, neuroscience, and philosophy of mind. This review systematically examines the principal theoretical frameworks proposing quantum-level mechanisms in neural computation, including the Orchestrated Objective Reduction (Orch OR) hypothesis by Penrose and Hameroff, the quantum brain hypothesis of Stapp, quantum coherence models in microtubules, and quantum field theories of consciousness. We critically evaluate the current state of empirical evidence—including recent quantum biology findings, the challenge of decoherence in warm, biological systems, and experimental observations of quantum effects in biological systems—alongside computational models that bridge quantum formalism and neural network dynamics. Furthermore, we discuss the implications of quantum cognition models for understanding perception, decision-making, and memory. Despite significant scientific skepticism, emerging evidence from quantum biology and advanced neuroimaging technologies provides tentative support for quantum processes in neural substrates. We conclude by identifying critical gaps in current knowledge and outlining future research directions that may resolve the debate, including proposals for experimental paradigms using quantum sensing technologies applied to neural tissue. This review aims to serve as a foundational reference for researchers across disciplines approaching the quantum mind hypothesis with scientific rigor.
ABSTRACT Recent advances at the intersection of quantum physics, neuroscience, and cognitive science have renewed interest in whether quantum phenomena and quantum‐inspired formalisms can inform models of human cognition and decision‐making. While classical neural and computational models explain many aspects of perception and behavior, they face limitations in accounting for contextuality, order effects, probabilistic reasoning violations, ambiguity resolution, and rapid information integration. This review synthesizes evidence and theoretical frameworks linking quantum‐related principles to cognition, with emphasis on quantum sensing in biological systems, molecular and subcellular quantum‐relevant mechanisms, quantum‐to‐classical transition pathways, quantum‐inspired models of perception and decision‐making, and quantum‐inspired computational approaches. It also extends beyond the probabilistic arm of quantum cognition by considering emerging equilibrium‐based and causal‐logical theories, including mental equilibrium, brain‐universe similarity, mind‐light‐matter unity, quantum emergence, and quantum intelligence. Throughout, the review distinguishes physically instantiated quantum mechanisms from abstract quantum‐like formalisms that model contextuality, mental entanglement, and non‐separable cognitive dynamics without requiring sustained macroscopic quantum coherence in the brain. Finally, it identifies methodological challenges and future directions for advancing quantum cognitive science toward experimentally testable and biologically meaningful theories.
Recent interdisciplinary research has raised interest in whether non-classical physical principles may impose fundamental constraints on how neural information can be observed, extracted, or decoded. While conventional neuroscience models neural signaling primarily through classical electrochemical processes, a growing body of theoretical literature has speculated that quantum-mechanical concepts-such as coherence, entanglement, or quantum-inspired information processing-may offer alternative perspectives on the limits of neural observability. This article provides a critical and integrative review of theoretical proposals situated at the intersection of neuroscience, quantum biology, and information theory, without assuming the physical realizability of quantum information processing in biological neural systems. We examine conceptual motivations, key physical constraints (including decoherence, thermal noise, and system complexity), and unresolved theoretical challenges that arise when extending classical neural decoding frameworks toward non-classical regimes. Rather than proposing an experimentally validated mechanism for brain decoding, this work focuses on identifying conceptual boundaries, potential misinterpretations, and open questions that must be addressed before non-classical approaches to neural signal decoding can be meaningfully evaluated. Ethical considerations, methodological limitations, and future research directions are discussed to clarify the conditions under which such speculative frameworks may contribute to neuroscience, while avoiding overextension beyond current empirical evidence.
Modern neuroscience research faces critical computational bottlenecks. Neural recordingtechnology advances allow for increasingly large, multidimensional datasets which containnon-stationary signals and complex nonlinear interactions across multiple spatiotemporalscales. At the same time, classical computing approaches are reaching their physicallimitations. The convergence of these two factors creates an urgent need for alternativecomputational approaches. Here, we argue that quantum computing offers transformativesolutions in three different ways. First, quantum algorithms and their implementation onquantum computing hardware can reveal novel neural features, including subtle dynamics andemergent network properties, that remain computationally inaccessible to classical methods.Indeed, recent findings in other scientific fields have already uncovered properties thatclassical approaches have failed to capture. Second, quantum systems provide exponentialscaling advantages for analyzing high-dimensional neural data. This includes demonstratedsignificant speedups for network state evaluation and exponentially improved efficiency indetecting correlations across sparse, noisy datasets which are typical of neuroscienceresearch. Third, quantum formalism offers alternative mathematical frameworks for understanding neural information processing. These frameworks better account for context-dependence, probabilistic dynamics, and multi-pathway causation than classical deterministicmodels. While current pre-fault tolerant devices face limitations in scalability and decoherence,developing hybrid quantum-classical approaches already show practical advantages in otherareas of research. Therefore, beyond computational speedups, quantum approaches mayfundamentally transform how we understand activity in the brain, neural informationprocessing, and the resulting cognitive phenomena.
Abstract The quantum-like paradigm has emerged over the last decade to describe non-linear, dynamical, complex phenomena using quantum mechanics as a tool. In essence, it takes advantage of the linearity of quantum information processing, allowing for complex correlations through entanglement. In a quantum- and neuroscience truly interdisciplinary research, we found that an open quantum spin network, mapping a neural system, can successfully simulate the human sense of number as a global dynamical property, in contrast with the poor performance of conventional Artificial Neural Networks. Here, we discuss how the simulation can be extended to other important complex perceptual phenomena like the perception of space, time, and numbers, known to be interdependent with each other, suggesting that a shared neuronal mechanism is operating in the brain. Here we present a research program that aims at creating a quantum toolbox to simulate this integrated space-time-number sensory ability of our brain, with open-quantum systems methods. We will explore the implications of more general quantum-matter paradigms, and their possible coding into a quantum technology.