In this article, the authors describe the structure of their proposed model, in which the processing of uncertainties arising in the process of the activities of organizations and enterprises is carried out with the help of artificial intelligence. A knowledge model is built based on existing facts about risks using imitation and encapsulation borrowed from object-oriented design. The processing of the obtained data takes place as a result of the construction of this model with the involvement of methods of the most promising and advanced direction in modern scientific research in various fields - quantum computing.
In this article, the authors describe the structure of their proposed model, in which the processing of uncertainties arising in the process of the activities of organizations and enterprises is carried out with the help of artificial intelligence. A knowledge model is built based on existing facts about risks using imitation and encapsulation borrowed from object-oriented design. The processing of the obtained data takes place as a result of the construction of this model with the involvement of methods of the most promising and advanced direction in modern scientific research in various fields - quantum computing.
The article is devoted to the study of the possibility of uncertainty processing using quantum algorithms. Uncertainty processing is one of the most important tasks in artificial intelligence. The quality of knowledge used by the intelligent system depends on the solution of this problem. Domain knowledge contains uncertainty. The paper considers the possibility of uncertainty handling using quantum computing. Tensor method is used to represent uncertainty. The tensor method provides an object representation of fuzzy knowledge. This approach allows to investigate the properties of uncertainty which are not available in representing fuzzy knowledge by traditional methods such as fuzzy sets, Bayesian networks, Stanford confidence factor, neural networks. Tensor processing on a classical computer is a computationally complex task. Quantum algorithms provide exponential acceleration in solving such problems. The article discusses the possibility of using quantum algorithms to process fuzzy knowledge represented by tensors.
This article discusses the development trends of artificial intelligence and quantum physics, as well as their role in modern science and technology. The fundamental concepts of quantum mechanics, the operating principles of quantum computing systems, and their advantages in data processing are analyzed. In addition, the study examines the new opportunities emerging from the integration of artificial intelligence algorithms with quantum technologies, their efficiency in solving complex computational problems, and promising future directions. The findings indicate that the integration of artificial intelligence and quantum technologies serves as an important factor in the advancement of scientific research and various applied fields.
Due to the rapid development of artificial intelligence (AI) technologies, marketing professionals are faced with a changing landscape that requires a comprehensive understanding of the potential and challenges of AI applications. Quantum computing processes are the basis for the future development of predictive analytics, as their ability to simultaneously provide many variants of events with different variations of their occurrence allows achieving reliability of up to 99.75%, even in conditions of irrationality. The relevance of the research is related to the state priority for the development of quantum technologies, as well as technological advances, in particular, ease of use and accuracy of the data obtained. The research is aimed at determining the combination of AI technologies with the use of quantum computing processes that is most suitable for the tasks of marketing analytics in order to increase the accuracy of forecasting consumer behavior and market mechanisms in the long term. The methodological basis of the research was scientific work in the field of marketing strategies, artificial intelligence, as well as descriptions of the basics of quantum computers, practical work of leading centers for the implementation of quantum computing in business processes. The theoretical basis is bibliometric analysis, theory of quantitative methods, theory of innovation. For the purposes of the research, literature was analyzed and various AI software products were systematized, a list of cases was provided and practical results in the field of quantum computing application in predictive analytics in the field of marketing were compared. Their impact on such marketing components as market analysis and forecasting, audience sentiment analysis, and offer personalization is assessed. The information base consisted of a study of 160 articles written in the field of predictive analytics based on AI for marketing purposes, as well as an assessment of 26 cases of the use of quantum computing in business processes both in Russia and abroad. The key focus of the research is a critical assessment of the benefits of implementing artificial intelligence technologies in conjunction with quantum computing processes in shaping marketing strategies, including long—term forecasts of consumer behavior and changes in market mechanisms. The result of the research is a framework for the introduction of artificial intelligence technologies in conjunction with quantum computing processes in the formation of marketing strategies.
Abstract— Studies in the literature argue that solving the memory problem of quantum computers (QC) will lead to groundbreaking developments in artificial intelligence algorithms affecting every process, strategy, business, and polic,es. Quantum adaptive algorithms have been widely applied, which improve existing methods by taking advantage of artificial intelligence and quantum computing. Although new developments are achieved with hybrid quantum systems, which aim to have sub-threads made by quantum computers, it cannot be predicted precisely where these studies will reach in the future and what kind of benefits and risks, they involve. In this study, based on the knowledge of contemporary and interdisciplinary literature, the current and potential uses of quantum computers, super artificial intelligence applications, and quantum computing methods on machine learning, whose applications have just begun in the laboratory environment, are examined, and assessed. It is claimed that the hardware disadvantages of quantum computers will disappear, and new quantum applications will be developed in the future. This is a requirement of adaptation to the prevalent usage of IoT in Industry 4.0 and Society 5.0 applications. Therefore, theoretically, and conceptual aspects of AI and quantum computing are evaluated to realize guidance of the miracles of the Prophets that can be discovered in the future.
This article deals with the problem of the uncertainty in rule-based systems (RBS), but from the perspective of quantum computing (QC). In this work we first remember the characteristics of Quantum Rule-Based Systems (QRBS), a concept defined in a previous article by one of the authors of this paper, and we introduce the problem of quantum uncertainty. We assume that the subjective uncertainty that affects the facts of classical RBSs can be treated as a direct consequence of the probabilistic nature of quantum mechanics (QM), and we also assume that the uncertainty associated with a given hypothesis is a consequence of the propagation of the imprecision through the inferential circuits of RBSs. This article does not intend to contribute anything new to the QM field: it is a work of artificial intelligence (AI) that uses QC techniques to solve the problem of uncertainty in RBSs. Bearing the above arguments in mind a quantum model is proposed. This model has been applied to a problem already defined by one of the authors of this work in a previous publication and which is briefly described in this article. Then the model is generalized, and it is thoroughly evaluated. The results obtained show that QC is a valid, effective and efficient method to deal with the inherent uncertainty of RBSs.
The urgency of conducting research in the field of quantum informatics is grounded. Promising areas of research are highlighted. For foreign and Russian publications and materials, a review of the main scientific results that characterize the current state of research in quantum computer science is made. It is noted that knowledge and funds are invested most intensively in the development of the architecture of a quantum computer and its elements. Despite the fact that today there is no information on the creation of a physical implementation of a quantum computer comparable in functionality to a classical digital computer, the development of quantum algorithms is one of the popular areas of research. An advantage of quantum algorithms is the fact that they reduce the time required to solve the problem due to the parallelization of operations by generating entangled quantum states and their subsequent use. This advantage (quantum acceleration) is most important when solving the problem of modeling the dynamics of complex systems and enumerated mathematical problems. (The general case of enumeration is the Grover scheme and its variants; the tasks of searching for hidden periods: Shor's scheme of using the fast quantum Fourier transform and its analogues.) The demand for cybersecurity developments (search for vulnerabilities in smart spaces, secure storage and use of big data, quantum cryptography) is noted. More than a dozen articles are devoted to quantum algorithms of key search, key distribution on optical fibers of various lengths, and the analysis of quantum resources necessary for conducting a cyber attack. In the field of artificial quantum intelligence, attention is paid, first of all, to the “search” for a model of a quantum neural network that is optimal from the point of view of using all the advantages presented by quantum computing and neural networks, as well as machine learning algorithms. Examples of the use of quantum computing in cognitive and social sciences for studying the decision-making mechanism with incomplete data are given. It is concluded that quantum informatics is promising for the simulation of complex natural and artificial phenomena and processes.