The possibility of using quantum correlations in the process of exchanging information between artificial intelligence systems has been considered. The benefits of using quantum correlations in such process include: high degree of information protection both on the physical level (electric and magnetic noise does not affect quantum correlations) and on the level of confidentiality preservation of information transmitted (any external interference results in data corruption without information disclosure); the possibility of full automation of information transmission process; the process is dissipation free (which follows from that the quantum correlations are performed between the quantum obj ects described by the same wave function); high speed of information transmission (higher than the speed of light). In this case the special relativity postulate concerning the speed of light is not violated, because it relates to inertial systems only; however, quantum correlation due to being dissipation free is an inertialess process (which follows from the well-known, experimentally-verified relationship between mass m and energy E: E = mc 2 , where с is the speed of light). Feasible approaches to using quantum correlations for information exchange between artificial intelligence systems have been reviewed in the paper, in particular two versions of information transmission between artificial intelligence systems have been considered: with external synchronization of information transmission time and autonomous synchronization performed by interacting artificial intelligence systems. The main difficulty in implementing information transmission by means of quantum correlations is the necessity of using the so-called entangled quantum objects, that is the quantum objects having some mutually-dependent characteristics of their wave functions.
The possibility of using quantum correlations in the process of exchanging information between artificial intelligence systems has been considered. The benefits of using quantum correlations in such process include: high degree of information protection both on the physical level (electric and magnetic noise does not affect quantum correlations) and on the level of confidentiality preservation of information transmitted (any external interference results in data corruption without information disclosure); the possibility of full automation of information transmission process; the process is dissipation free (which follows from that the quantum correlations are performed between the quantum obj ects described by the same wave function); high speed of information transmission (higher than the speed of light). In this case the special relativity postulate concerning the speed of light is not violated, because it relates to inertial systems only; however, quantum correlation due to being dissipation free is an inertialess process (which follows from the well-known, experimentally-verified relationship between mass m and energy E: E = mc 2 , where с is the speed of light). Feasible approaches to using quantum correlations for information exchange between artificial intelligence systems have been reviewed in the paper, in particular two versions of information transmission between artificial intelligence systems have been considered: with external synchronization of information transmission time and autonomous synchronization performed by interacting artificial intelligence systems. The main difficulty in implementing information transmission by means of quantum correlations is the necessity of using the so-called entangled quantum objects, that is the quantum objects having some mutually-dependent characteristics of their wave functions.
The field of Quantum Artificial Intelligence (Quantum AI) has emerged as a promising interdisciplinary research area where the computational power of quantum computing is merged with the learning and decision-making power of AI. The application of quantum principles such as superposition, entanglement and quantum interference to machine learning algorithms has created new possibilities for solving problems that may be difficult to solve by classical machines. In the last few years, advances in Noisy Intermediate-Scale Quantum (NISQ) devices have helped to speed advances in Quantum Machine Learning (QML) algorithms, Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Convolutional Neural Networks (QCNNs) and hybrid learning algorithms. This review provides a comprehensive overview of the research in the field of Quantum AI from 2020 to 2026, covering the key concepts, novel architectures, algorithmic advancements, and practical applications. A critical assessment of the application of Quantum AI in fields such as healthcare, finance, cybersecurity, scientific computing and optimization problems is provided, and the benefits of quantum-enhanced learning models are discussed. In addition, common difficulties such as quantum hardware limitations, sensitivity to noise, encoding of data, scalability constraints and a lack of speed in training variational quantum circuits are discussed. The review also covers new avenues of research, including trustworthy quantum machine learning, explainable Quantum AI, quantum federated learning, and fault-tolerant quantum computing. The results suggest while practical large-scale deployment is currently limited by existing hardware capabilities, hybrid quantum-classical systems have great potential to rapidly move intelligent data processing and develop next-generation AI systems.
Quantum computing and artificial intelligence are two highly topical fields of research that can benefit from each other’s discoveries by opening a completely new scenario in computation, that of quantum artificial intelligence. Indeed, on the one hand, artificial intelligence algorithms can be made computationally more efficient due to the potential speedup enabled by quantum phenomena; on the other hand, the complex development of quantum computing technologies and methodologies can be properly supported by the use of classical artificial intelligence approaches. The “entanglement” of these two disciplines is opening up completely new directions in computer science research, and this survey aims to provide a systematic and taxonomic overview of the work that has already been done and that which will begin in the near future.
The object of this PhD Thesis is light and matter quantum interaction including studies on cavity and circuit quantum electrodynamics (CQED and cQED), quantum information theory and entanglement dynamics in open systems. In recent years both theoretical and experimental efforts have been devoted to study problems regarding the transfer of quantum correlations, quantum memories, entanglement protection against decoherence and new regimes accessible in cQED. One of the fundamental model underlying these topics is the well known Jaynes-Cummings (JC) one for light-matter interaction, introduced in quantum optics already in 1963. This paradigmatic model describes the coherent exchange of a single excitation between a qubit (a two-level system) and a mode of a quantized harmonic oscillator. The JC model describes a lot of purely quantum effects such as Rabi oscillations and generation of entangled quantum states, which were confirmed by several sophisticated experiments in CQED. This model has been then applied in many other frameworks. Analytical results can be derived only under the so-called rotating wave approximation (RWA), which allows to neglect the energy contributions coming from the counter-rotating terms, which describe the simultaneous excitation (or de-excitation) of the qubit and the field mode. In this Thesis a first interesting investigation, based on the JC model, is related to the realization of quantum memories. The main aspect is the transfer of generic quantum entangled states of a propagating radiation to some qubits, such as two-level atoms placed in separated optical cavities. Generalizing previous results for bipartite entangled systems, I investigated the more complex case of tripartite systems, where the very quantification of entanglement is still an open problem. This issue is broadened to open systems in contact with a common environment which induces decoherence effects unavoidable in realistic implementations. The main result of this analysis is that it is possible to map in an optimal way a quantum state from radiation to qubits in the unitary dynamics, and even in the presence of a dissipative environment a significative amount of entanglement can be transferred. This study has been carried on with either theoretical calculations or numerical simulations performed adopting the powerful Monte Carlo wave function method. A second important problem for quantum memories faced in this PhD Thesis, regards the protection of qubits entanglement for long enough times in order to implement quantum information tasks. A possible way to obtain this goal is to add an external coherent field driving two or more two-level atoms interacting with a common mode of a cavity electromagnetic field. In fact it is possible to demonstrate that, under certain conditions, an effective Hamiltonian of the whole system involves, together with the familiar JC terms, also the counter-rotating ones. The main feature that comes out from this analysis is the possibility to freeze, during system dynamics, peculiar quantum entangled states of the atomic qubits in so called decoherence-free subspaces. This important property allows to store the entanglement in matter qubits, isolating the quantum state from environment induced decoherence. Though the system includes dissipation, analytical results have been obtained for multipartite entanglement and decoherence by solving the master equation through a projection on a suitable basis and using the method of characteristics. In particular it is possible to monitor the rate of decoherence via measurements of joint atomic probabilities, shading light on the fascinating border between the quantum and the classical world. A last topic investigated in this Thesis concerns the recently developed and quite promising implementation of the JC model well outside CQED, that is cQED. Here superconductive circuits, which play the role of artificial atoms, interact on-chip with transmission line resonators, instead of the usual electromagnetic cavities. These systems have the advantage of being quite easily engineered exploiting the most advanced available technologies. Moreover they allow a great flexibility in handling the relevant parameters, since the inter-system coupling is here of capacitive or inductive nature, instead of the usual electric dipole interaction in CQED. In this way a strong and ultrastrong coupling regime have become available, giving a direct access to the physics beyond the JC model and its possible applications. I first performed a detailed characterization of system dynamics in order to test the main approximations usually introduced to describe superconductive circuits as qubits. Then I described, both analytically and numerically, the dissipative dynamics of a superconductive qubit coupled to a resonator mode in the deep strong regime.
An exponential rise in the demand of secured information processing requires an alternative approach from the conventional classical world, and the present scientific community believes that one of the most distinctive physical resources of the quantum world is quantum entanglement. It is a unique correlation between the components of quantum multipartite systems, which has never been observed in the classical macroscopic world. It is a tool that not only explains the effectiveness and fruitfulness of quantum information processing compared to its classical counterpart but also becomes significant from the security point of view when quantum communication is considered. The most trusted and investigated resource till date is quantum entanglement, for the purpose of secured data communication. Similar to energy, quantum entanglement is a physical resource connected to the odd nonclassical connections that can occur between distant quantum systems. Using a pair of entangled quantum systems, it is feasible to carry out computing operations that are impractical for conventional systems. Quantum information theory is a broad study of the information processing capabilities of quantum systems.
Introduction. The paper is devoted to the digitalization of the industry and the safety of construction. The urban economy includes systems, for example, water supply and sanitation, HVAC and others, the reliability and stability of which are closely related to the level of cybersecurity. In the context of digitalization and the widespread adoption of BIM and automated process control systems, the level of their exposure to cyber threats is significantly increasing. Traditional approaches to security are not effective enough. The practical application of quantum communications and artificial intelligence methods in the field of cybersecurity remains poorly understood. The purpose of this study is to analyze the potential of using quantum technologies and artificial intelligence methods to improve the cybersecurity of BIM and automated process control systems, as well as to assess the prospects for implementing solutions to protect critical infrastructure. Materials and methods. The work uses methods of system analysis, modelling of cybersecurity architectures, as well as machine learning methods to identify anomalies in network traffic and telemetry of BIM and automated process control systems. The technology of quantum key distribution, which provides physically guaranteed confidentiality of communication channels, is considered as a cryptographic basis. Results. A hybrid architecture of BIM and automated process control systems cyber defense technologies is proposed, combining quantum technologies to protect communications and intelligent monitoring based on machine learning to detect abnormal behavior. It is shown that the integration of quantum networks with AI can significantly increase the stability of the system, as well as reduce the time for incident detection. Conclusions. The results of the study confirm the prospects for the joint use of quantum communications and artificial intelligence to protect BIM, automated process control systems. The proposed approach can be used in the design of cybersecurity systems and serves as the basis for further research in this area.
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.