Abstract Perceptual memory is important for human cognition and learning because it plays an important role in forming consciousness, memories, learning, and stimulus awareness. Perceptual memory assists in the recognition of items or faces, as well as in the sorting, differentiating, and combining of new information with context and emotions. Perceptual-associative memory encodes information structurally, semantically, and phonetically. It interacts with the conscious artifact's medium-term memory (MTM), long-term memory (LTM), workspace, and emotion modules to construct meanings and connections to chosen sensory information. This research developed a quantum cognitive model of perceptual associative memory to replicate human-like perceptual cognitions in conscious artifacts. Moreover, for the quantum neural correlates of consciousness, this research has also simulated the circuit of the quantum neural network for non-linear learning of an XOR gate using the qiskit library.
Abstract Perceptual memory is important for human cognition and learning because it plays an important role in forming consciousness, memories, learning, and stimulus awareness. Perceptual memory assists in the recognition of items or faces, as well as in the sorting, differentiating, and combining of new information with context and emotions. Perceptual-associative memory encodes information structurally, semantically, and phonetically. It interacts with the conscious artifact's medium-term memory (MTM), long-term memory (LTM), workspace, and emotion modules to construct meanings and connections to chosen sensory information. This research developed a quantum cognitive model of perceptual associative memory to replicate human-like perceptual cognitions in conscious artifacts. Moreover, for the quantum neural correlates of consciousness, this research has also simulated the circuit of the quantum neural network for non-linear learning of an XOR gate using the qiskit library.
The article presents a review of foreign scientific literature on the quantum-like approach to understanding the modeling of cognition and decision-making processes. It is shown that the current application of quantum physics approaches to consciousness, psychological approaches such as perception, cognition, emotions, consciousness and learning, involves the search for a single objective mathematical model of what underlies the experience of the brain. The use of quantum physics algorithms can help in understanding the mechanisms of brain malfunctions and memory impairments that are found in pathologies such as dementia caused by Alzheimer's disease, schizophrenia, etc. Quantum physics approaches are applicable to issues of modeling cognition and decision-making processes, as well as in cognitive research, information processing by the human brain, artificial intelligence development, psychology, social and political sciences. Application of the abstract mathematical principles of quantum theory can help in understanding human consciousness and behavior. Quantum probability theory is applied to modeling human behavior due to the prevalence of uncertainty in human interaction with the world. Quantum cognitive models are especially useful in research in such areas as errors in decision making, order effects of questions, conceptual combinations, accumulation of evidence, perception, etc. Of particular interest to researchers are issues of quantum-like modeling of cognition and decision-making processes, the apparatus of quantum information and probability theory, useful for such modeling, as well as the use of such apparatus in cognitive research, the development of artificial intelligence, psychology, social and political sciences.
The Essence of the Project our goal is not simply to invent a device or program, but rather to create an integrated system that preserves, simulates, and develops human consciousness at the quantum level, so that unique knowledge, experiences, and memories are not lost upon the death of the biological body. We can summarize the idea in three pillars: Mind Digitization Scanning a person's neural architecture (connectome) with nanoscale precision. Encoding the brain's electrochemical dynamics in a mathematical form amenable to quantum simulation. Cosmic Memory Bank A quantum data warehouse to store human memory (personal and collective) with near-instantaneous access time. Protecting data with unbreakable quantum encryption. Integrating individual data into a "collective consciousness" library that can be used by any future generation or civilization. Quantum Artificial Intelligence (Quantum AI) Acts as a conscious assistant system capable of operating and simulating stored mental patterns. Not just a data processor, but a virtual mind capable of creativity, self-learning, and cognitive expansion. 2. Project Technical Architecture A. Quantum Brain-Computer Interface (QBCI) Relies on ultra-sensitive nanoscale neural sensors to read electrochemical states with Planck-time precision. Using quantum entanglement to instantly link brain signals to quantum processing units. B. Quantum Simulation Model of Consciousness Representing neurons as quantum qubits instead of traditional bits. Simulating neural interconnections as quantum circuits with multi-path probabilities (superposition). Combining dynamic brain physics models with quantum self-learning algorithms. C. Quantum Memory Bank Quantum servers distributed across data centers on Earth and beyond (satellites, space stations). Hierarchical Archiving System: Core Memory: Identity, values, core experiences. Contextual Memory: Experiences, emotions, temporal and spatial context. Adaptive Memory: Continuous updates and learning after digitization. 3. Near and Long-Term Goals Near Term (10–15 years) Create a partial experimental model to simulate consciousness on a limited scale (an animal brain or part of the human brain). Develop a stable and secure quantum brain-computer interface. Build a prototype quantum memory bank. Long-Term (50–100 years) Preserve and transfer human consciousness across biological, digital, and quantum media. Enabling "consciousness migration" across space—i.e., transferring minds to robots or artificial biological bodies on other planets. Create a universal consciousness library, where civilizations share their data and experiences via space-based quantum networks.
According to recent research on the brain and cognition, the microtubule level activities in the brain are in accordance with the quantum mechanical concepts. Consciousness is the emergent phenomenon of the brain’s subsystems and the quantum neural correlates. Based on the global work-space theory and traditional neural networks, investigations in machine consciousness and machine intelligence are reporting new techniques. In this study, a novel approach using circuit-based quantum neural network is proposed and simulated. This approach satisfies all the criteria of quantum computing and is tested for the exclusive OR (XOR) gate’s nonlinear learning. As a result of the use of quantum gates, various quantum circuits, such as quantum adders and subtractors, are also created and included in the designing and simulation of circuit of the quantum neural networks. Moreover, it is also argued that the proposed circuit of quantum neural network may also be tested and implemented on real quantum computer hardware. The present study also stresses the applicability of techniques of machine learning algorithms such as quantum and classical neural networks to various challenges of High Energy Physics.
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.
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.