Recently, quantum biology and molecular quantum computation have attracted substantial attention. Quantum biology applies quantum mechanics to biological systems at the molecular scale. Molecular quantum computing explores the degrees of freedom of molecules that can be used to produce quantum coherence, such as charge, orbital, opto-spin (interplay between optical excitation and spin), vibration, and rotation, to process quantum information. Cognitive science focuses on understanding how learning processes are realized, particularly within the human brain. The most common topic among these three is the computational process, which can exploit different levels of representation, either classical or quantum. Here, we review progress in quantum biology, molecular quantum computing, and quantum theory in cognitive science. Based on our critical analysis and review, we highlight that molecular quantum computing could be an important bridging research area between quantum biology and a deeper understanding of neuronal cells in cognitive science. Thus, these three areas can be the core to understanding how the classical world emerges from the quantum world and human intelligence. To answer these questions, we may gain insight by studying the quantum processes that underlie biological systems, such as photosynthesis and enzyme catalysis. An unprecedented opportunity for molecular quantum computing is to perform functionalities similar to those of the human brain. In this manner, we could not only expand the boundaries for quantum computing but also gain a better understanding of cognitive processes.
Recently, quantum biology and molecular quantum computation have attracted substantial attention. Quantum biology applies quantum mechanics to biological systems at the molecular scale. Molecular quantum computing explores the degrees of freedom of molecules that can be used to produce quantum coherence, such as charge, orbital, opto-spin (interplay between optical excitation and spin), vibration, and rotation, to process quantum information. Cognitive science focuses on understanding how learning processes are realized, particularly within the human brain. The most common topic among these three is the computational process, which can exploit different levels of representation, either classical or quantum. Here, we review progress in quantum biology, molecular quantum computing, and quantum theory in cognitive science. Based on our critical analysis and review, we highlight that molecular quantum computing could be an important bridging research area between quantum biology and a deeper understanding of neuronal cells in cognitive science. Thus, these three areas can be the core to understanding how the classical world emerges from the quantum world and human intelligence. To answer these questions, we may gain insight by studying the quantum processes that underlie biological systems, such as photosynthesis and enzyme catalysis. An unprecedented opportunity for molecular quantum computing is to perform functionalities similar to those of the human brain. In this manner, we could not only expand the boundaries for quantum computing but also gain a better understanding of cognitive processes.
The emergence of quantum computing has prompted researchers to examine potential parallels between quantum mechanical principles and human cognitive processes. This article explores the theoretical framework of quantum cognition, examining how concepts from quantum mechanics, including superposition, entanglement, and measurement, might inform our understanding of human decision-making, memory, and consciousness. While quantum computing operates on fundamentally different physical principles than biological neural networks, the mathematical formalisms of quantum mechanics offer novel perspectives on cognitive phenomena that classical probability theory struggles to explain. This review critically examines the current state of quantum cognition research, its applications in computational modeling, and the ongoing debates regarding the validity of quantum mechanical analogies in psychological theory. The analysis reveals both promising theoretical insights and significant limitations in applying quantum principles to human cognition, highlighting the need for empirical validation and careful distinction between mathematical analogy and physical reality.
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.
Background: Quantum biology is an emerging interdisciplinary field that explores how principles of quantum mechanics-such as superposition, entanglement, tunneling, and collapse-may influence biological phenomena.Initially speculative, it now offers concrete explanations for complex life processes, prompting a re-evaluation of biology at the quantum scale.Method: This is a narrative review based on an analysis of peer-reviewed literature from indexed journals.Key biological processes were examined in the context of quantum principles.Applications in virology, microbiology, neuroscience, and pharmacology were explored.Discussion: The integration of quantum mechanics into drug discovery and personalized medicine is reshaping therapeutic approaches.Quantum computing promises accelerated simulations of molecular interactions and enzyme dynamics.Quantum dot-based therapies and diagnostics are enhancing precision medicine.The role of superradiance and quantum coherence in neuroprotection, particularly in Alzheimer's disease, is gaining research traction.Techniques such as photodynamic therapy (PDT) and repetitive transcranial magnetic stimulation (rTMS) are being reconsidered through the lens of quantum biology.Conclusion: Quantum biology bridges quantum mechanics and life sciences, offering novel explanations for long-standing biological mysteries.Despite significant experimental challenges-especially in warm, complex biological environments-the field is advancing through ultrafast spectroscopy, cryo-electron microscopy, and quantum simulations.Continued interdisciplinary collaboration and technological innovation are essential to validate these quantum-biological interactions.Ultimately, quantum biology holds the potential to revolutionize our understanding of life, leading to groundbreaking developments in neuroscience, pharmacology, and precision medicine.
This paper explores the potential of quantum computing for simulating and predicting biological processes. The core concept involves translating complex biological systems, specifically molecular interactions, into quantum systems represented by quantum states. These quantum states are then subjected to simulation and prediction using quantum computing algorithms. The work aims to address the inherent limitations of classical computing in accurately modeling quantum phenomena within biological systems, such as those involved in photosynthesis, enzyme catalysis, and perhaps even consciousness. We present a framework for this approach, highlighting the challenges and opportunities involved. Specifically, we investigate the applicability of quantum algorithms – like Variational Quantum Eigensolver (VQE) and Quantum Monte Carlo – to model biological systems. The ultimate goal is to quantify the probability of specific biological events occurring, leading to a fundamentally new approach to biological research and drug discovery. This research proposes a novel methodology for leveraging the power of quantum computation to unlock the secrets of life.
Abstract Quantum technologies leverage the laws of quantum physics to achieve performance advantages in applications ranging from computing to communications and sensing. They have been proposed to have a range of applications in biological science. This includes better microscopes and biosensors, improved simulations of molecular processes, and new capabilities to control the behavior of biomolecules and chemical reactions. Quantum effects are also predicted, with much debate, to have functional benefits in biology, for instance, allowing more efficient energy transport and improving the rate of enzyme catalysis. Conversely, the robustness of biological systems to disorder from their environment has led to proposals to use them as components within quantum technologies, for instance as light sources for quantum communication systems. Together, this breadth of prospective applications at the interface of quantum and biological science suggests that quantum physics will play an important role in stimulating future biotechnological advances. This review aims to provide an overview of this emerging field of quantum biotechnology, introducing current capabilities, future prospects, and potential areas of impact. The review is written to be accessible to the non‐expert and focuses on the four key areas of quantum‐enabled sensing, quantum‐enabled imaging, quantum biomolecular control, and quantum effects in biology.
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.