This review presents an overview of cognitive systems, quantum phenomena and possible connections between them. The focus will be the artificial cognitive systems and briefly touch the discussion of possible benefits from quantum counterparts. The nonclassical features of Quantum Theory introduced as quantum resources which enables possible speed ups or advantages over classical computational tasks. Quantum computation is introduced as a powerful computational tool over its classical counterparts by also covering possible applications of cognitive phenomena in the framework of quantum cognition. Also different attempts in order to implement decision making processes for cognitive purposes mentioned.
This review presents an overview of cognitive systems, quantum phenomena and possible connections between them. The focus will be the artificial cognitive systems and briefly touch the discussion of possible benefits from quantum counterparts. The nonclassical features of Quantum Theory introduced as quantum resources which enables possible speed ups or advantages over classical computational tasks. Quantum computation is introduced as a powerful computational tool over its classical counterparts by also covering possible applications of cognitive phenomena in the framework of quantum cognition. Also different attempts in order to implement decision making processes for cognitive purposes mentioned.
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.
Quantum computing is necessary to be described as a powerful tool is the new generation of computational power that works based on the principles of quantum mechanics, allowing the solving of problems that are intractable for classical computers. Due to the utilization of such phenomena as superposition, quantum entanglement and quantum interference, quantum computers are said to be able to solve problems in such areas as cryptography, artificial intelligence, optimization problems and material sciences, among others. This paper attempts to examine the basic concepts of quantum computing while focusing on their different aspects and their potential impacts. This study its experience and potential in its current form and in future applications through examples, illustrations, and theoretical discussion. Specific problems, including error correction, hardware scalability, and requirements for resources to address, make the paper a comprehensive overview of the quantum computing potential and weaknesses. Moving towards practical examples, the discussion covers all aspects of the book, providing readers with a holistic understanding of how quantum computing is going to change technological paradigms in the forthcoming decades.
Quantum cognition is a new field in cognitive science, which is characterized by the application of quantum probability theory, quantum dynamics, and quantum information processing to account for human behavior in cognitive tasks. This chapter provides an introduction to the basic principles and a review of applications of these principles to a wide range of cognitive tasks. The power of quantum cognition comes from using the same principles to coherently link together a wide range of phenomena that have never been previously connected together.
Quantum Machine Learning (QML) has emerged as a groundbreaking approach within the rapidly evolving field of Quantum Computing (QC), leveraging quantum phenomena such as superposition and entanglement to surpass classical models in representational power, optimization speed, and computational efficiency. This paradigm shift paves the way for transformative applications across diverse domains while simultaneously confronting the inherent complexities of quantum mechanics and hardware limitations. Among the powerful computational models used for decision-making and pattern recognition, Fuzzy Cognitive Maps (FCMs) have demonstrated exceptional capabilities in modeling complex systems. Researchers have continually refined the traditional FCM framework by integrating novel features to enhance its effectiveness. However, despite its potential, the integration of quantum computing with FCMs remains largely unexplored, with only a limited number of studies addressing this intersection. This work provides a comprehensive theoretical algorithmic evaluation of the integration between Quantum Computing and FCMs, investigating potential advancements, challenges, and novel applications. Additionally, we present a systematic review of existing research on Quantum Fuzzy Cognitive Maps (QFCMs), offering new insights into their theoretical foundations and future directions. By bridging the gap between quantum computing and cognitive modeling, this study aims to unlock new possibilities for intelligent decision-making systems in the quantum era.
Experiments in cognitive science and decision theory show that the ways in which people combine concepts and make decisions cannot be described by classical logic and probability theory. This has serious implications for applied disciplines such as information retrieval, artificial intelligence and robotics. Inspired by a mathematical formalism that generalizes quantum mechanics the authors have constructed a contextual framework for both concept representation and decision making, together with quantum models that are in strong alignment with experimental data. The results can be interpreted by assuming the existence in human thought of a double-layered structure, a 'classical logical thought' and a 'quantum conceptual thought', the latter being responsible of the above paradoxes and nonclassical effects. The presence of a quantum structure in cognition is relevant, for it shows that quantum mechanics provides not only a useful modeling tool for experimental data but also supplies a structural model for human and artificial thought processes. This approach has strong connections with theories formalizing meaning, such as semantic analysis, and has also a deep impact on computer science, information retrieval and artificial intelligence. More specifically, the links with information retrieval are discussed in this paper.