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Panhellenic Conference on Informatics

Research of Implementation Methods for Quantum Machine Learning and Their Development on QPU

Viktor Potapov, Sergey M. Gushanskiy

Published Nov 25, 2022• 0 citations
Publisher

Abstract

Over the past few decades, there has been a significant breakthrough in the field of quantum computing. Research is of growing interest, which has recently led to the development of quantum information systems prototypes and methods for their development. The paper describes the possibilities, advantages and disadvantages of quantum neural networks. The analysis of throughput, learning complexity and comparison of a classical feed-forward neural network and a quantum neural network with two different feature maps is carried out.

DOI
10.1145/3575879.3576023

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Over the past few decades, there has been a significant breakthrough in the field of quantum computing. Research is of growing interest, which has recently led to the development of quantum information systems prototypes and methods for their development. The paper describes the possibilities, advantages and disadvantages of quantum neural networks. The analysis of throughput, learning complexity and comparison of a classical feed-forward neural network and a quantum neural network with two different feature maps is carried out.
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In recent years, quantum computing has emerged as a potentially gamechanging technology, with applications across various disciplines, including AI and machine learning.In recent years, the combination of quantum computing and neural networks has led to the development of quantum neural networks (QNNs).This paper explores the potential of QNNs and their applications in solving complex problems that are challenging for classical neural networks.This paper explores the fundamental principles of quantum computing, the architecture of QNNs, and their advantages over classical neural networks.Furthermore, this will highlight key research areas and challenges in the development and utilization of QNNs.Through an in-depth analysis, it demonstrates the QNNs hold significant promise for addressing complex computational problems and advancing the field of artificial intelligence.
The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep learning and quantum-inspired deep learning techniques in recent times. In this work, we present an overview of advances in the intersection of quantum computing and deep learning by discussing the technical contributions, strengths and similarities of various research works in this domain. To this end, we review and summarise the different schemes proposed to model quantum neural networks (QNNs) and other variants like quantum convolutional networks (QCNNs). We also briefly describe the recent progress in quantum inspired classic deep learning algorithms and their applications to natural language processing.
The usage of QNNs is one approach that might be used to close the knowledge gap that exists between the domains of machine learning and quantum computing. As a result of this research, a full analysis of QNNs has been offered, covering a wide range of issues including their theoretical underpinnings, training techniques, benefits, limits, applications, and assessment methodologies. Quantum neural networks, also known as QNNs, are a type of artificial neural network that makes use of the power of quantum computing. These networks provide the promise for a significant improvement in processing speed as well as greater representation capabilities. On the other hand, in order to make full advantage of the benefits that QNNs give, obstacles such as scalability concerns and hardware restrictions need to be addressed and fixed. The fact that QNNs have the potential to be applied in such a broad range of different sectors is evidence of both the adaptability and the significance of this rapidly developing field of study. It will be necessary to do more study and investigation if there is to be any hope of making headway in the field of quantum machine learning with regard to the creation of QNNs and of realizing their full potential.
Machine learning techniques give impressive results in many areas. However, due to the physical limitation of integrated circuits which restricts their computational power growth, and the rapid advances in quantum computing, lots of research studies on quantum machine learning (QML) have been done recently. QML is a technique that uses quantum algorithms as parts of the implementation. Quantum algorithms use quantum mechanics and have the potential to outperform classical algorithms for a given problem. In this paper, three widely used machine learning algorithms are discussed and their quantum versions are presented, namely: quantum neural network, quantum autoencoder, and quantum kernel method. In addition, we discuss the potential capabilities of these QML algorithms and review recent work employing them. Moreover, a quantum neural network prototype is implemented using Qiskit as a proof of concept and tested on a real quantum computer. Empirical results show that quantum neural networks can be trained efficiently.
Recently, there has been a rapid increase interest in quantum computers. Their work is based on the use of quantum-mechanical phenomena such as superposition and entanglement for computing to transform input data into outputs, which can actually provide effective performance 3-4 orders of magnitude higher than any modern computing devices. In the past few decades, there has been an acute problem of creating a quantum computer that uses quantum mechanical effects for computations. Currently, there are prototypes of devices of this class, but it is not yet possible to achieve an effective solution to the planned tasks with their help due to a number of difficulties in implementation. However, there is a mathematical apparatus that describes the behavior of quantum particles using a wave function. Hence, it becomes possible to simulate quantum computations on the classical architecture and check the reliability of algorithms belonging to the NP class, which are theoretically executed on a quantum computer in polynomial time and which cannot be solved on classical computers within an acceptable time frame. Effectively simulate the operation of a quantum system on devices with classical architecture is impossible, it remains to propose methods for acceleration of quantum computing on classical systems.