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.
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.
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks
Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
This article presents the most common image recognition algorithms implemented for a quantum neural network, consider them and draw a conclusion which algorithm is preferable to use in this or that case. Currently, there is a rapid increase in interest in quantum algorithms for pattern recognition, since the first quantum computers have already appeared. This is because the use of quantum algorithms since the advent of quantum computers has allowed an exponential increase in the speed of solving NP-complete problems. Paper decide the some problems of finding and creation of approaches for the functioning of quantum calculation processes and prototypes of quantum devices in the framework of quantum image processing. A method for storing and processing image components in quantum systems is proposed, which potentially will give a significant step toward in efficiency in comparison with existing classical ones and provide a solution to a number of complex problems.
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.
Quantum computation has attracted numerous attentions and develops rapidly in the recent decades. To against the decoherence and the control errors upon the qubits, quantum error corrections are adopted. Such approaches require lots of redundant qubits, accurate measurement and timely feedback. Here we investigate a new framework of quantum computation that is associated with fuzzy processing. It will benefit significantly from three aspects: the fuzzy recognition of qubit states reduce the required gate fidelity; the fuzzy encoding encodes the information of the qubits into a distribution of probability, suppressing the fluctuations in the output of long quantum circuits; the fuzzy feedback offers a more efficient way to control the qubits when precision information of quantum states are absent. Furthermore, the fuzzy processing can be integrated into quantum error correction, eliminating the need for immediate correction operations. The proposed scheme will be fairly suitable for the solution of decision problems, which has significant applications in the optimization problems and control problems.
Quantum computing has emerged as a transformative technology with the potential to revolutionize artificial intelligence (AI) and machine learning (ML). This paper explores the intersection of quantum computing and AI, focusing on how quantum principles can enhance computational capabilities and address challenges in traditional machine learning approaches. Key aspects discussed include quantum algorithms such as quantum support vector machines, quantum neural networks, and quantum variational algorithms, which leverage quantum superposition and entanglement to process vast amounts of data more efficiently than classical counterparts. These algorithms promise to accelerate tasks such as optimization, pattern recognition, and data classification, thereby advancing the capabilities of AI systems. Moreover, quantum computing offers potential breakthroughs in solving combinatorial optimization problems that are computationally intensive for classical computers. Quantum annealing and other quantum optimization techniques are explored for their application in AI, providing novel approaches to solving complex decision-making problems.