The article considers the fundamental question of the non-computable nature of algorithms that could serve as a basis for modeling brain consciousness and, therefore, for the develop-ment of artificial intelligence systems.The use of relativistic algorithms to overcome the Turing barrier is proposed.Quantum and relativistic algorithms are compared.The advantage of relativistic algorithms is shown
The article considers the fundamental question of the non-computable nature of algorithms that could serve as a basis for modeling brain consciousness and, therefore, for the develop-ment of artificial intelligence systems.The use of relativistic algorithms to overcome the Turing barrier is proposed.Quantum and relativistic algorithms are compared.The advantage of relativistic algorithms is shown
Information is a part of the physical universe as matter and energy. Today first small quantum computers begin to appear. A quantum computer represents information by qubits. In quantum computation there are two principles (algorithms) that speed up the computation, Fourier Transform and Grover's amplification algorithm. Quantum artificial intelligence is introduced by linear algebra-based quantum machine learning and symbolical quantum artificial intelligence. The initial motivation for relating quantum theory to consciousness was related to free free will in a deterministic world, since the quantum world is nondeterministic. The dualism of mind and matter involves the unresolved problem of how the mind and brain could interact. The relationship of union between the mind and the body of a person is resolved by quantum measurement. Sir Roger Penrose argues that elementary acts of consciousness cannot be described by a Turing machine. Such non-countable elementary acts of conscious ness are only present in the wave function collapse (measurement). Penrose assumes that the wave function collapse is not random, it represents an objective reduction.
Abstract Artificial intelligence algorithms, fueled by continuous technological development and increased computing power, have proven effective across a variety of tasks. Concurrently, quantum computers have shown promise in solving problems beyond the reach of classical computers. These advancements have contributed to a misconception that quantum computers enable hypercomputation, sparking speculation about quantum supremacy leading to an intelligence explosion and the creation of superintelligent agents. We challenge this notion, arguing that current evidence does not support the idea that quantum technologies enable hypercomputation. Fundamental limitations on information storage within finite spaces and the accessibility of information from quantum states constrain quantum computers from surpassing the Turing computing barrier. While quantum technologies may offer exponential speed-ups in specific computing cases, there is insufficient evidence to suggest that focusing solely on quantum-related problems will lead to technological singularity and the emergence of superintelligence. Subsequently, there is no premise suggesting that general intelligence depends on quantum effects or that accelerating existing algorithms through quantum means will replicate true intelligence. We propose that if superintelligence is to be achieved, it will not be solely through quantum technologies. Instead, the attainment of superintelligence remains a conceptual challenge that humanity has yet to overcome, with quantum technologies showing no clear path toward its resolution.
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.
In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence.
We approach the question "What is Consciousness?" in a new way, not as Descartes' "systematic doubt", but as how organisms find their way in their world. Finding one's way involves finding possible uses of features of the world that might be beneficial or avoiding those that might be harmful. "Possible uses of X to accomplish Y" are "Affordances". The number of uses of X is indefinite (or unknown), the different uses are unordered, are not listable, and are not deducible from one another. All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by the organism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions: (1) Artificial general intelligence based on universal Turing machines (UTMs) is not possible, since UTMs cannot "find" novel affordances. (2) Brain-mind is not purely classical physics for no classical physics system can be an analogue computer whose dynamical behaviour can be isomorphic to "possible uses". (3) Brain mind must be partly quantum-supported by increasing evidence at 6.0 sigma to 7.3 sigma. (4) Based on Heisenberg's interpretation of the quantum state as "potentia" converted to "actuals" by measurement, where this interpretation is not a substance dualism, a natural hypothesis is that mind actualizes potentia. This is supported at 5.2 sigma. Then mind's actualizations of entangled brain-mind-world states are experienced as qualia and allow "seeing" or "perceiving" of uses of X to accomplish Y. We can and do jury-rig. Computers cannot. (5) Beyond familiar quantum computers, we discuss the potentialities of trans-Turing-systems.
Quantum artificial intelligence is an important step forward in the relationship with both quantum computing and AI. This paper investigates the use of quantum computing technique as an augmentation to AI capabilities efficiencies in data retrieval, model development, and problem resolution. A very general description is provided of self-consistence concepts of quantum computing, the main milestones in the field of quantum artificial intelligence, and practical analogs in a range of areas. Algorithm development, the development of hardware, and ethics are the obstacles that are mentioned, which then give some perspectives concerning the future within this new field.