The objective of this paper is to relate the concept of intelligence to the first principles of physics, and, in particular, to answer the following question: can AI system composed only of physical components compete with a human? The answer is proven to be negative if the AI system is based only on simulations, and positive if digital devices are included. It has been demonstrated that in mathematical world, the bridge from matter to intelligence requires extension and modification of quantum physics. Such an extension is implemented by quantum neural nets that perform simulations combined with digital punctuations. The universality of this quantum-classical hybrid is in capability to violate the second law of thermodynamics by moving from disorder to order without external resources. This advanced capability that could be accepted as a definition of human-like intelligence is illustrated by examples.
The objective of this paper is to relate the concept of intelligence to the first principles of physics, and, in particular, to answer the following question: can AI system composed only of physical components compete with a human? The answer is proven to be negative if the AI system is based only on simulations, and positive if digital devices are included. It has been demonstrated that in mathematical world, the bridge from matter to intelligence requires extension and modification of quantum physics. Such an extension is implemented by quantum neural nets that perform simulations combined with digital punctuations. The universality of this quantum-classical hybrid is in capability to violate the second law of thermodynamics by moving from disorder to order without external resources. This advanced capability that could be accepted as a definition of human-like intelligence is illustrated by examples.
The challenge of this work is to connect physics with the concept of intelligence. By intelligence we understand a capability to move from disorder to order without external resources, i.e. in violation of the second law of thermodynamics. The objective is to find such a mathematical object described by ODE that possesses such a capability. The proposed approach is based upon modification of the Madelung version of the Schrodinger equation by replacing the force following from quantum potential with non-conservative forces that link to the concept of information. A mathematical formalism suggests that a hypothetical intelligent particle, besides the capability to move against the second law of thermodynamics, acquires such properties like self-image, self-awareness, self- supervision, etc. that are typical for Livings. However since this particle being a quantum-classical hybrid acquires non-Newtonian and non-quantum properties, it does not belong to the physics matter as we know it: the modern physics should be complemented with the concept of the information force that represents a bridge to intelligent particle. As a follow-up of the proposed concept, the following question is addressed: can artificial intelligence (AI) system composed only of physical components compete with a human? The answer is proven to be negative if the AI system is based only on simulations, and positive if digital devices are included. It has been demonstrated that there exists such a quantum neural net that performs simulations combined with digital punctuations. The universality of this quantum-classical hybrid is in capability to violate the second law of thermodynamics by moving from disorder to order without external resources. This advanced capability is illustrated by examples. In conclusion, a mathematical machinery of the perception that is the fundamental part of a cognition process as well as intelligence is introduced and discussed. The discovery of isolated dynamical systems that can decrease entropy in violation of the second law of thermodynamics, and resemblances of these systems to livings implies that Life can slow down heat death of the Universe, and that can be associated with the purpose of Life.
The challenge of this work is to connect quantum mechanics with the concept of intelligence. By intelligence we understand a capability to move from disorder to order without external resources, i.e. in violation of the second law of thermodynamics. The objective is to find such a mathematical object described by ODE that possesses such a capability. The proposed approach is based upon modification of the Madelung version of the Schrodinger equation by replacing the force following from quantum potential with non-conservative forces that link to the concept of information. A mathematical formalism suggests that a hypothetical intelligent particle, besides the capability to move against the second law of thermodynamics, acquires such properties like self-image, self-awareness, self- supervision, etc. that are typical for Livings. However since this particle being a quantum-classical hybrid acquires non-Newtonian and non-quantum properties, it does not belong to the physics matter as we know it: the modern physics should be complemented with the concept of an information force that represents a bridge to intelligent particle. It has been suggested that quantum mechanics should be complemented by the intelligent particle as an independent entity, and that will be the necessary step to physics of Life. At this stage, the intelligent particle is introduced as an abstract mathematical concept that is satisfied only mathematical rules and assumptions, and its physical representation is still an open problem.
Contemporary discussions of the gap between natural and artificial intelligence often emphasize human capacities such as contextual reasoning, cognitive flexibility, and non-classical decision-making. This paper proposes that quantum and quantum-like models of cognition and decision processes offer a principled framework for addressing these differences. A growing body of empirical evidence shows that human reasoning systematically violates the assumptions of classical probability and logic, exhibiting contextuality, order effects, interference phenomena, and task incompatibility. Quantum probability theory and related quantum-like formalisms provide mathematically rigorous tools—based on Hilbert spaces, superposition, and entanglement—that capture these features more naturally than classical models. While quantum and quantum-like approaches share a common mathematical structure, they differ in physical implementation, motivating two complementary directions in artificial intelligence: quantum AI and quantum-like AI. Together, these approaches suggest a viable pathway toward narrowing, and potentially bridging, the divide between natural and artificial intelligence by grounding AI architectures in models aligned with the structure of human cognition.
Quantum Artificial Intelligence (QAI) combines quantum computing with artificial intelligence (AI) to surpass the traditional barriers of classical computing systems, such as their inability to solve large-scale optimization problems or simulate complex quantum systems, which require exponentially increasing computational resources. Quantum computing, on the other hand, makes use of quantum mechanical phenomena of superposition, entanglement, and interference to process millions of data points in parallel, avoiding the exponential computation needed in many complex problems. QAI combines principles of quantum computing with AI techniques, thereby accelerating model learning. Some of the most notable applications include drug discovery in terms of exact predictions of molecular interactions, due to the enhancement of quantum simulations. There are cryptographic applications where QAI challenges classical encryption but also contains more advanced, non-breakable quantum cryptographic methods. In the same way as general advances in the field of data analysis and optimization, these domains would change: financial modeling, cybersecurity, and climate science. The biggest challenges remaining in scaling quantum hardware are noise and decoherence. Solutions will require major advances in quantum error correction, along with hybrid quantum-classical systems integrating the strengths of each. At this point, added to the list of critical concerns in the development of QAI are ethical considerations such as data privacy, algorithmic bias, and transparency. But all this notwithstanding, QAI has the transformative potential to solve problems that classical AI was unable to solve and drive innovation in domains ranging from healthcare and environmental science to cryptography and beyond. Unlocking its full capabilities and overcoming the resultant technical and ethical challenges is by way of further research and collaboration.
The intersection of artificial intelligence (AI) and quantum physics has sparked significant advancements in both fields. This paper first reviews the application of AI techniques, particularly neural networks, to quantum simulation. We discuss key developments in representing quantum states, efficient sampling methods, and optimization techniques for ground states and dynamics. The review also explores how quantum principles enhances AI algorithms, including quantum-inspired neural architectures and physics-based optimization methods. We highlight challenges and open questions in this rapidly evolving interdisciplinary field, emphasizing the potential for future innovations that combine AI and quantum approaches to address complex problems in both domains.
Abstract The gap between natural and artificial intelligence is often discussed in terms of creativity, contextual adaptability, and non-algorithmic decision-making capacities where human cognition appears fundamentally different from current AI systems. This paper argues that developing quantum and quantum-like models of cognition, decision-making, and AI provides a promising pathway for narrowing, and perhaps essentially bridging, this gap. Empirical studies of human cognition and decision-making reveal systematic deviations from classical probability, logic, and information theory—manifesting as contextuality, order effects, interference (such as conjunction and disjunction effects), task incompatibility, and apparent randomness. These phenomena are well captured by quantum probability theory and related quantum-like frameworks, which provide a rigorous mathematical formalism—Hilbert spaces, superposition, entanglement, and decoherence—for modeling cognitive states and their evolution. Such models go beyond metaphor, showing that aspects of human reasoning can be more faithfully represented using quantum-like rather than classical probabilistic structures. Although genuine quantum (based on quantum physics) and quantum-like approaches share the same mathematical foundation, they differ experimentally. Both stimulate the development of novel AI architectures: quantum AI (QAI) and quantum-like AI (QLAI). While QAI depends on advances in quantum computing, QLAI can be realized on classical digital or analog hardware. The advancement of both offers a promising route to reducing—and potentially bridging—the divide between natural and artificial intelligence. This paper sets out a conceptual program to unify natural and artificial intelligence via quantum/quantum-like models of consciousness/cognition and AI.