Pioneering work into Very Large Scale Integration (VLSI) and the theory of computation reveals a new revolution. Quantum physics is essential to the understanding of deuces with two stable states and to how theoretically, classical Turing computation is physically possible. Furthermore, completely novel quantum‐mechanical computational devices utilizing dimensional confinement can be made; similar mechanisms may well exist in natural biological systems and brains. Shows that this new quantum‐mechanical perspective yields the basis for a model of conscious machinery, with correspondingly well defined processes specifying the nature of perception, cognition and information, for which as yet computer science has no agreed definitions.
Pioneering work into Very Large Scale Integration (VLSI) and the theory of computation reveals a new revolution. Quantum physics is essential to the understanding of deuces with two stable states and to how theoretically, classical Turing computation is physically possible. Furthermore, completely novel quantum‐mechanical computational devices utilizing dimensional confinement can be made; similar mechanisms may well exist in natural biological systems and brains. Shows that this new quantum‐mechanical perspective yields the basis for a model of conscious machinery, with correspondingly well defined processes specifying the nature of perception, cognition and information, for which as yet computer science has no agreed definitions.
In recent decades it has become common throughout the social sciences to think of the human mind simply as a very complex computer. Drawing on an older materialist metaphor of the mind as a machine, the “computational” model of the mind took off after the mid-century invention of the computer and subsequent cognitive revolution in psychology, and has since permeated the social sciences in the form of rational choice and other prominent social theories. The contributions of this model have been huge; but it has always been assumed that the computations going on inside our heads are classical. Quantum brain theory challenges this assumption by proposing that the mind is actually a quantum computer. Classical computers are based on binary digits or “bits” with well defined values (0 or 1), which are transformed in serial operations by a program into an output. Quantum computers in contrast are based on “qubits” that can be in superpositions of 0 and 1 at the same time and also interact non-locally, enabling every qubit to be operated on simultaneously. The idea of a quantum computer was first proposed in the 1980s and is still far from being realized technologically, but it has fired the imagination of scientists by raising the prospect of unimaginable increases in computational power, and other neat tricks besides. If quantum brain theory is true, then our model of the mind would be similarly radically altered, irrespective of the issue of consciousness. Quantum brain theory hypothesizes that quantum processes at the elementary level are amplified and kept in superposition at the level of the organism, and then, through downward causation constrain what is going on deep within the brain. On this view, information from the environment is continuously transformed from the macro- to the micro-level, and then channeled back upward into an “internal quantum state,”
The physics and the mathematics of computation are examined to provide a foundation and perspective for the investigation of the quantum mechanics of computation. Our purpose is to explore the fundamental limits and constraints imposed on computation by Nature through the laws of physics and the mathematics of computational complexity. Inasmuch as information storage and transmission are an integral part of computation, their physical bounds are considered. The computer is viewed both physically and mathematically as a dynamical system, and is depicted in terms of the basic Turing machine paradigm. Three fundamental classes of the Turing machine are defined; the deterministic, stochastic and quantum Turing machines. Hamiltonian models and physical realizations of quantum computing are described. Quantum computers can perform some tasks which have no classical analogue, but they cannot compute functions that are non-computable by classical means. Some classically intractable problems can be solved with quantum computers.
We extend an empirically grounded theory of the existence of quantum computer. The main question that we are considering is a question about the quantum computer’s possibility of existence and creation. As empirical evidence, we use logic, which we show in cognitive perspective. For a definition of the computer, we use a formal definition of Turing machine. By formulating many definitions abstractly and phenomenologically we go around the areas of quantum physics, quantum computing and other quantum-related fields of science which could give an unambiguous answer to our question about the essence of quantum computer and which is developed so they can’t. In many ways, it makes this theory about the existence of quantum computer universal for these areas, although less applied for them. We consider some corollary of the essences of quantum computer, including the possibility of quantum computer for the human. References to research of cognitive nature of logic suggest empirical basis which we follow in our theory.
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.
Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.
Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.