Understanding the structures of human semantic memory search has been a critical initiative in cognitive science.One testing method is the semantic memory task, which asks patients to recall as many words as possible within a specific category, often to test for neurological disorders.Recent studies have shown that classical computers can simulate brain mechanisms using random behaviors.But unlike classical models, which assume that semantic activation spreads along a single probabilistic pathway at a time, quantum random walks allow for simultaneous exploration of multiple conceptual associations, reflecting the parallel and context-dependent retrieval mechanisms observed in cognition through the use of superposition and interference.To address this, we introduce a quantum random walk computation approach for more detailed modeling to address this.By developing the first quantum computing procedure for classical INVITE random walks using quantum random walks, we provide the maximum probabilities of generating semantically related word categories pertinent to the semantic fluency task in assessing a patient's potential semantic memory recall for cognitive diseases.Thus, this method promises the potential for various quantum search algorithms and offers greater scalability in statistical analysis.
Understanding the structures of human semantic memory search has been a critical initiative in cognitive science.One testing method is the semantic memory task, which asks patients to recall as many words as possible within a specific category, often to test for neurological disorders.Recent studies have shown that classical computers can simulate brain mechanisms using random behaviors.But unlike classical models, which assume that semantic activation spreads along a single probabilistic pathway at a time, quantum random walks allow for simultaneous exploration of multiple conceptual associations, reflecting the parallel and context-dependent retrieval mechanisms observed in cognition through the use of superposition and interference.To address this, we introduce a quantum random walk computation approach for more detailed modeling to address this.By developing the first quantum computing procedure for classical INVITE random walks using quantum random walks, we provide the maximum probabilities of generating semantically related word categories pertinent to the semantic fluency task in assessing a patient's potential semantic memory recall for cognitive diseases.Thus, this method promises the potential for various quantum search algorithms and offers greater scalability in statistical analysis.
This paper investigates the application of quantum walk algorithms to large-scale graph search problems. Classical algorithms often struggle with the computational complexity of exploring vast graphs, particularly when searching for specific patterns or nodes. Quantum walks, inspired by the probabilistic nature of random walks, provide a promising alternative by utilizing quantum superposition and interference to accelerate the search process. We explore the theoretical framework and potential advantages of employing quantum walks for graph traversal, focusing on scenarios where exponential speedups are theoretically possible. This work aims to demonstrate the potential of quantum computing to tackle computationally demanding graph search tasks that are currently intractable for classical methods. The core concept leverages the ability to simultaneously explore multiple paths within a graph, drastically reducing the search time compared to sequential exploration. We present a foundational understanding of quantum walk implementation and analyze its suitability for various graph search algorithms.
The representation and processing of concepts is one of the hardest challenges in cognitive science. While computer scientists and engineers have focused on developing advances for particular tasks, philosophers and cognitive scientists have focused on elucidating the structural nature of meaning. A remarkable bridge between these two limited-success approaches can be found in behavioral research, since, in a variety of tasks, humans process information at a conceptual level in a way that is incompatible with classical probability and fuzzy set theory. Recently, attempts have been made to use mathematical schemes founded on quantum structures as alternative approaches. For this reason, the application of quantum structures to this type of phenomena has received increasing attention. The quantum approach allows to faithfully model a number of non-classical deviations observed in experimental data. Moreover, it shows that genuine quantum theoretical notions, such as contextuality, superposition, emergence, and entanglement, are powerful epistemic tools to understand and represent cognitive phenomena. In this thesis, we identify the limitations of classical theories to handle some important cognitive tasks, and introduce the fundamentals of the quantum cognitive approach to concepts. Next, we perform a mathematical analysis of current concept combination models and develop an extension that allows for concrete representations of multiple exemplars simultaneously. Our analysis indicates that a superposition of logical reasoning and a specific form of non-logical reasoning, where non-logical reasoning is dominant, allows to faithfully represent the experimental data. Therefore, the non-logical reasoning introduced by this model represents an important but unexplored form of reasoning in humans. In addition, we develop novel experimental methodologies to identify quantum conceptual structures for concept combinations in the context of natural language processing and psychological experiments. Namely, we present a methodology to build entangled concepts represented as sets of words with respect to a corpus of text, and present a computational and psychological methodology to discern if a collection of concepts behaves statistically as a collection of quantum or classical particles. Using both methodologies we have identified a significant presence of quantum conceptual structure in the context of natural language processing and psychological experiments.
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.
In the world of computational probing, random walks and quantum walks are two separate but tightly linked paradigms. Classical stochastic processes like random walks are based on the random movement of particles in mathematical space and have been extensively used in data mining, network analysis, computer vision, etc. Quantum walks, by contrast generalize random walks into the quantum realm, taking the benefit of the quantum superposition and interference in order to investigate multiple paths at the same time. Quantum walks provide significant potential for algorithmic development: in quantum computing and communication, and in Scientific modelling and simulation. This paper provides an in-depth survey of random walks and quantum walks, outlining historical context, fundamental principles, applications, challenges, and future prospects. In this paper, motivated by the abstractness of both random walks and quantum walks, we survey the field of computational exploration at large scales, coalescing the myriad notions and difficult challenges addressed by existing research efforts on random and quantum walks, inferring a nuanced and multifaceted view on how both random walks and quantum walks are involved in paving the way to future development in many fields.
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.
A connection between quantum theory and human memory came out of cybernetics research in the early 1950s (von Foerster, 1950). It was not until modelling the activation of a word in human memory did the connection begin to appear in mainstream psychological literature. Target word activation was speculated as being similar to quantum entanglement because the target word and its associative network were being activated “in synchrony,” the core intuition behind the “spooky-action-at-a-distance” formula (Nelson et al ., 2003). This chapter begins by developing a quantum-like model of the human mental lexicon and compares it against other memory models. Specific attention is paid to how these models describe the activation of a word in memory in the context of the extra- and intra-list cuing tasks. The chapter then covers how quantum theory can be applied to model human semantic space. The human mental lexicon A mental lexicon refers to the words that comprise a language, and its structure is defined here by the associative links that bind this vocabulary together. Such links are acquired through experience and the vast and semi-random nature of this experience ensures that words within this vocabulary are highly inter-connected, both directly and indirectly through other words. For example, the word planet becomes associated with earth, space, moon , and so on, and within this set, moon can become linked to earth and star . Words are so associatively interconnected with each other they meet the qualifications of a “small world” network wherein it takes only a few associative steps to move from any one word to any other in the lexicon (Steyvers & Tenenbaum, 2005).