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International Conference on Communication, Computer Sciences and Engineering (IC3SE)

A Detailed Overview of Quantum Computing Machine Learning Techniques

Ada Aggarwal, S Vikram Singh, Saloni Bansal, Vijay Bhutani

Published May 9, 2024• 24 citations
Publisher

Abstract

The computing sector has undergone radical changes in the last few decades. Conventional computers use binary (1s and 0s) numbers to perform tasks that are specified by the user. Using the concepts of quantum physics, a unique method known as “quantum computing” addresses problems that are too complicated for conventional computing devices. Two of the scientific fields with the fastest growth rates in today's time are machine learning and quantum computing. Researchers have recently initiated investigations into the ways in which conventional machine learning methods can be improved by quantum computing. Quantum machine learning comprises hybrid approaches that combine quantum and classical algorithms, using quantum methods to evaluate quantum states rather than traditional data. Current data science techniques could be significantly improved by quantum algorithms. In this work, we summarize the contributions made by different researchers in the domain of Quantum Learning and then look at some of the methods related to its real-world uses.

DOI
10.1109/ic3se62002.2024.10593221

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The computing sector has undergone radical changes in the last few decades. Conventional computers use binary (1s and 0s) numbers to perform tasks that are specified by the user. Using the concepts of quantum physics, a unique method known as “quantum computing” addresses problems that are too complicated for conventional computing devices. Two of the scientific fields with the fastest growth rates in today's time are machine learning and quantum computing. Researchers have recently initiated investigations into the ways in which conventional machine learning methods can be improved by quantum computing. Quantum machine learning comprises hybrid approaches that combine quantum and classical algorithms, using quantum methods to evaluate quantum states rather than traditional data. Current data science techniques could be significantly improved by quantum algorithms. In this work, we summarize the contributions made by different researchers in the domain of Quantum Learning and then look at some of the methods related to its real-world uses.
Over the course of the last seven decades the world of computing has taken revolutionary reformations in the scope of classical computing. Classical computers manipulate ones and zeroes to crunch through operations given to them by users. An innovative strategy known as quantum computing leverages the concepts of quantum physics to solve issues that are too complex for conventional computers to handle. Two of the domains of science that are now progressing the fastest are theoretical machine learning and quantum computing theory. In recent years, researchers have begun examining how classic machine learning techniques may be improved by quantum computing. Hybrid techniques that integrate conventional and quantum algorithms are part of quantum machine learning. Instead of using standard data, quantum techniques may be utilized to examine quantum states. However, quantum algorithms have the potential to greatly expand current data science techniques. In this paper we review the contribution carried out by various researchers in the field of Quantum Machine Learning and later we look at certain techniques associated with it for its use.
Recently, increased availability of the data has led to advances in the field of machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. Since quantum computers leverages the properties of quantum mechanics, it carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. This paper addresses the concepts of quantum computing which influences machine learning in a quantum world. It also states the speedup observed in different machine learning algorithms when executed on quantum computers. The paper towards the end advocates the use of quantum application software and throw light on the existing challenges faced by quantum computers in the current scenario.
Quantum computation and quantum information have sparked tremendous interest across a wide range of scientific disciplines, from physics to chemistry and engineering, as well as computer science, mathematics, and statistics. Data science is the application of statistical methodologies, computer algorithms, and domain scientific data to extract knowledge and insights from large amounts of data and solve complicated real-world issues. As a result, quantum machine learning has carved itself a distinct niche in the world of computers. When the potential of quantum computing characteristics is employed for machine learning, quantum technology advances to an advanced degree. When quantum computing capabilities are incorporated into standard methods, they give extraordinary parallel computing power for addressing complicated problems. The core of this work is a comparison of the fundamental principles of quantum computing and their superiority over traditional computing. This paper discusses application-based algorithms including QSVM, QPCA, and Q-KNN, as well as Grover's algorithm, the most common and foundational quantum machine learning technique.
Quantum computing with its inherent parallelism provides a quantum advantage over classical computing. Its potential to offer breakthrough advances in various areas of science and engineering is foreseen. Machine learning is one of the key areas where the power of quantum computing can be utilized. Though many machine learning algorithms have been successfully developed to solve a variety of problems in the past decades, these algorithms take a long time to train. Also working on today's colossal datasets makes these algorithms computationally intensive. Quantum machine learning by utilizing the concepts of superposition and entanglement promises a solution to this problem. Quantum machine learning algorithms are in surface for the past few years and majority of the current research has dealt with the two machine learning problems namely classification and clustering. In this paper, a brief review of the recent techniques and algorithms of quantum machine learning and its scope in solving real world problems is studied.
In recent times, increasing amount of the data have enriched the decision making using machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. A few research efforts have demonstrated that quantum computers which leverages the properties of quantum mechanics, carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. The paper focuses on the concepts of quantum computing that would be used by any machine learning technique to facilitate quantum machine learning. It also studies different quantum algorithms that could be used as core subroutines in machine learning techniques and highlights the famous Grovers algorithm. These subroutines are used to enhance the efficacy of machine learning tasks. The paper towards the end advocates the use of quantum application software and throws light on the existing challenges faced by quantum computers in the current scenario.
Over the past several decades, computing has undergone revolutionary changes, moving beyond classical systems that manipulate ones and zeroes to solve complex problems. One key area where quantum computing power is particularly promising is machine learning. While conventional machine learning algorithms have successfully addressed a diverse set of challenges, they are often computationally intensive and slow, while working on today's massive datasets. Quantum machine learning aims to solve these challenges by enhancing classical methods with quantum algorithms, offering faster processing and more efficient data analysis. In recent years, research has focused on applying quantum techniques to classic machine learning tasks. This paper reviews key contributions from researchers in quantum machine learning and explores its potential to revolutionize real-world problem-solving by integrating both traditional and quantum methods.