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Advanced Science

Quantum‐Like Dynamics in Whole‐Brain Models of the Human Connectome

Gustavo Deco, Yonatan Sanz Perl, Natasha Greenstein, Shamil Chandaria, Gregory D. Scholes, Morten L. Kringelbach

Published Aug 18, 2026• 2 citations
Publisher

Abstract

Emerging research provides evidence for quantum-like (QL) probability laws, including interference effects, in non-quantum physical systems using coupled oscillators. In principle, such systems could produce QL states able to support QL computation in biological tissue. In this perspective, we propose a new avenue of research, namely to investigate if this is true in the human brain. Intriguingly, the special topology of human brain anatomy could promote the rich dynamical repertoire in human cognition. As a proof-of-principle, we investigated this by constructing two whole-brain models with and without QL dynamics, where the QL regime is distinguished by a larger whole-brain spectral gap. In accordance with our hypothesis, we found that the QL regime provides a significantly better fit to large-scale human empirical neuroimaging data and exhibits a lower model-derived energy cost than the non-QL model. Furthermore, we constructed a whole-brain model measuring the Växjö Interference term, a measure of functional quantum-like interference showing a significant correlation with the size of the spectral gap. Overall, these results suggest that quantum-like whole-brain models may provide a useful description of large-scale resting-state brain dynamics, and we set out what would be required to test whether this description also carries explanatory value for brain function.

DOI
10.1002/advs.77103

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Emerging research provides evidence for quantum-like (QL) probability laws, including interference effects, in non-quantum physical systems using coupled oscillators. In principle, such systems could produce QL states able to support QL computation in biological tissue. In this perspective, we propose a new avenue of research, namely to investigate if this is true in the human brain. Intriguingly, the special topology of human brain anatomy could promote the rich dynamical repertoire in human cognition. As a proof-of-principle, we investigated this by constructing two whole-brain models with and without QL dynamics, where the QL regime is distinguished by a larger whole-brain spectral gap. In accordance with our hypothesis, we found that the QL regime provides a significantly better fit to large-scale human empirical neuroimaging data and exhibits a lower model-derived energy cost than the non-QL model. Furthermore, we constructed a whole-brain model measuring the Växjö Interference term, a measure of functional quantum-like interference showing a significant correlation with the size of the spectral gap. Overall, these results suggest that quantum-like whole-brain models may provide a useful description of large-scale resting-state brain dynamics, and we set out what would be required to test whether this description also carries explanatory value for brain function.
We propose a {\it quantum-like} (QL) model of the functioning of the brain. It should be sharply distinguished from the reductionist {\it quantum} model. By the latter cognition is created by {\it physical quantum processes} in the brain. The crucial point of our modelling is that discovery of the mathematical formalism of quantum mechanics (QM) was in fact discovery of a very general formalism describing {\it consistent processing of incomplete information} about contexts (physical, mental, economic, social). The brain is an advanced device which developed the ability to create a QL representation of contexts. Therefore its functioning can also be described by the mathematical formalism of QM. The possibility of such a description has nothing to do with composing of the brain of quantum systems (photons, electrons, protons,...). Moreover, we shall propose a model in that the QL representation is based on conventional neurophysiological model of the functioning of the brain. The brain uses the QL rule (given by von Neumann trace formula) for calculation of {\it approximative averages} for mental functions, but the physical basis of mental functions is given by neural networks in the brain. The QL representation has a {\it temporal basis.} Any cognitive process is based on (at least) two time scales: subcognitive time scale (which is very fine) and cognitive time scale (which is essentially coarser).
We present a contextualist statistical realistic model for quantum-like representations in physics, cognitive science and psychology. We apply this model to describe cognitive experiments to check quantum-like structures of mental processes. The crucial role is played by interference of probabilities for mental observables. Recently one of such experiments based on recognition of images was performed. This experiment confirmed our prediction on quantum-like behaviour of mind. In our approach ``quantumness of mind'' has no direct relation to the fact that the brain (as any physical body) is composed of quantum particles. We invented a new terminology ``quantum-like (QL) mind.'' Cognitive QL-behaviour is characterized by nonzero coefficient of interference λ.λ.λ. This coefficient can be found on the basis of statistical data. There is predicted not only cos⁡θ\cos θcosθ-interference of probabilities, but also hyperbolic cosh⁡θ\cosh θcoshθ-interference. This interference was never observed for physical systems, but we could not exclude this possibility for cognitive systems. We propose a model of brain functioning as QL-computer (there is discussed difference between quantum and QL computers).
We discuss the conjecture on quantum-like (QL) processing of information in the brain. It is not based on the physical quantum brain (e.g., Penrose) - quantum physical carriers of information. In our approach the brain created the QL representation (QLR) of information in Hilbert space. It uses quantum information rules in decision making. The existence of such QLR was (at least preliminary) confirmed by experimental data from cognitive psychology; in particular, experiments on recognition of ambiguous figures (Conte et al., 2006; 2009). The violation of the law of total probability in these experiments is an important sign of nonclassicality of data. In this paper we speculate on a possible physical realization of QLR in the brain: a classical wave model producing QLR . It is based on variety of time scales in the brain. Each pair of scales (fine - the background fluctuations of electromagnetic field and rough - the cognitive image scale) induces the QL representation. The background field plays the crucial role in creation of “superstrong QL correlations” in the brain.
Quantum-like (QL) modeling, one of the outcomes of the quantum information revolution, extends quantum theory methods beyond physics to decision theory and cognitive psychology. While effective in explaining paradoxes in decision making and effects in cognitive psychology, such as conjunction, disjunction, order, and response replicability, it lacks a direct link to neural information processing in the brain. This study bridges neurophysiology, neuropsychology, and cognitive psychology, exploring how oscillatory neuronal networks give rise to QL behaviors. Inspired by the computational power of neuronal oscillations and quantum-inspired computation (QIC), we propose a quantum-theoretical framework for coupling of cognition/decision making and neural oscillations - {\it QL oscillatory cognition.} This is a step, may be very small, towards clarification of the relation between mind and matter and the nature of perception and cognition. We formulate four conjectures within QL oscillatory cognition and in principle they can checkedAsanoexperimentally. But such experimental tests need further theoretical and experimental elaboration. One of the conjectures (Conjecture 4) is on resolution of the binding problem by exploring QL states entanglement generated by the oscillations in a few neuronal networks. Our findings suggest that fundamental cognitive processes align with quantum principles, implying that humanoid AI should process information using quantum-theoretic laws. Quantum-Like AI (QLAI) can be efficiently realized via oscillatory networks performing QIC.
Quantum-like (QL) modeling, one of the outcomes of the quantum information revolution, extends quantum theory methods beyond physics to decision theory and cognitive psychology. While effective in explaining paradoxes in decision making and effects in cognitive psychology, such as conjunction, disjunction, order, and response replicability, it lacks a direct link to neural information processing in the brain. This study bridges neurophysiology, neuropsychology, and cognitive psychology, exploring how oscillatory neuronal networks give rise to QL behaviors. Inspired by the computational power of neuronal oscillations and quantum-inspired computation (QIC), we propose a quantum-theoretical framework for coupling of cognition/decision making and neural oscillations - QL oscillatory cognition. This is a step, may be very small, toward clarification of the relation between mind and matter and the nature of perception and cognition. We formulate four conjectures within QL oscillatory cognition and in principle they can be checked experimentally. But such experimental tests need further theoretical and experimental elaboration. One of the conjectures (Conjecture 4) is on resolution of the binding problem by exploring QL states entanglement generated by the oscillations in a few neuronal networks. Our findings suggest that fundamental cognitive processes align with quantum principles, implying that humanoid AI should process information using quantum-theoretic laws. Quantum-Like AI (QLAI) can be efficiently realized via oscillatory networks performing QIC.
Introduction In previous chapters, we discussed the conjecture of quantum-like (QL) processing of information in the brain. See, for instance, Chapter 1 (Section 1.16) and Chapter 3 (Section 3.1). In general, such processing need not be based on the physical quantum brain (see, for instance, Hameroff [1] [2] and Penrose [3] [4]), which is the quantum physical carrier of information. As we remarked in Khrennikov [5], the brain created the QL representation (QLR) of information in Hilbert space. Such representation uses quantum information rules in decision making. The existence of such QLR was (at least preliminary) confirmed by experimental data from cognitive psychology (please see Chapter 8). The violation of the law of total probability in these experiments is an important sign of the non-classicality of data. In the constructive wave function approach (see Section 8.16 of Chapter 8), such data can be represented by complex amplitudes. We also presented the QL model of decision making in Chapter 9. In this chapter we reproduce (with only slight modifications) the paper by Khrennikov [6]. The next natural step is to try to find possible physical realizations of the QLR in the brain. One of the possibilities is to appeal to the quantum physics of the microprocesses in the brain - the quantum brain. However, quite surprisingly, it is possible to proceed within the classical field framework, i.e. to create a classical wave model producing the QLR.