Quantum machine learning provides unprecedented scope in computing the techniques done in classical machine learning on a quantum computer. The entanglement and superposition of the basic qubit states promise to provide an edge over the performance and scope of classical machine learning.
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.
Quantum machine learning (QML) represents the fusion of quantum computing algorithms with machine learning techniques. A prominent application of QML involves employing machine learning algorithms to analyze classical data using a quantum computer, known as quantum-enhanced machine learning. This interdisciplinary field merges concepts from quantum physics and machine learning to pioneer new algorithms and methodologies for data processing and analysis. QML seeks to harness unique quantum properties like superposition and entanglement to enhance the performance of machine learning models. It explores the synergy between quantum computing and machine learning, aiming to leverage quantum systems’ inherent characteristics for advanced data processing capabilities. This chapter provides an in-depth exploration of QML’s comprehensive approach.
Quantum Machine Learning (QML) is the emerging confluence of quantum computing and artificial intelligence that promises to solve computational problems inaccessible to classical systems. Using quantum principles such as superposition, entanglement, and interference, QML promises exponential speed-ups and new paradigms for data processing in machine learning tasks. This review gives an overview of QML, from advancements in quantum-enhanced classical ML to native quantum algorithms and hybrid quantum-classical frameworks. It varies from applications in optimization, drug discovery, and quantum-secured communications, showcasing how QML can change healthcare, finance, and logistics industries. Even though this approach holds so much promise, significant challenges remain to be addressed-noisy qubits, error correction, and limitations in data encoding-that must be overcome by interdisciplinary research soon. The paper tries to collate the state of the art of QML in theoretical underpinnings, practical applications, and directions into the future.
Quantum Computing can be defined as an intersection among Quantum Physics, Computer Science and Mathematics. Though the behavior of quantum mechanics is spooky, the mathematics behind it is simpler and easier than classical one. Using superposition and entanglement, quantum computing can solve complex problems efficiently and it would take less amount of time than its classical counterpart to do so. Machine Learning (ML) is a subset of Artificial Intelligence (AI) which helps to learn from data, analyze patterns and make itself improved. When quantum computing is blended with Machine Learning, Quantum Machine Learning pops up. In this survey, we explore what Quantum Machine Learning (QML) is and how it actually works. We try to demonstrate the very quantum touch on our known machine learning algorithms. We also delve into the various application areas of QML. This paper helps to understand QML from basics to advanced levels.
Quantum machine learning (QML) represents a cutting-edge convergence of quantum computing and artificial intelligence, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance machine learning algorithms. By exploiting the computational advantages of quantum systems, QML offers the potential for exponential speedups in data processing, optimization, and pattern recognition tasks, particularly for high-dimensional and complex datasets. This article explores the foundational principles, algorithmic approaches, and practical applications of quantum machine learning across domains including finance, chemistry, drug discovery, optimization, and natural language processing. It discusses quantum-enhanced supervised and unsupervised learning, quantum neural networks, variational quantum algorithms, and hybrid quantum-classical architectures. Additionally, the article addresses challenges related to hardware limitations, noise, scalability, and interpretability, as well as the emerging trends in near-term quantum devices, quantum simulators, and integration with classical AI pipelines. Quantum machine learning holds the promise of revolutionizing computational intelligence, offering transformative capabilities for solving problems that are currently intractable for classical systems.
The paper "Quantum Machine Learning: Leveraging Quantum Computing for Enhanced Learning Algorithms" explores the integration of quantum computing principles into classical machine learning techniques, aiming to address limitations such as scalability and computational inefficiency. It presents the foundational concepts of quantum computing, including superposition and entanglement, and their application in accelerating machine learning processes. The study emphasizes the potential for quantum algorithms to significantly improve the performance of machine learning tasks by processing large datasets more efficiently and exploring larger hypothesis spaces. Key quantum machine learning algorithms discussed include Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum Neural Networks (QNN), each of which leverages quantum mechanics to overcome the computational barriers faced by classical algorithms. The Quantum Approximate Optimization Algorithm (QAOA) is also highlighted for its ability to optimize machine learning models more effectively. While the theoretical benefits of Quantum Machine Learning (QML) are promising, the practical application of these techniques is currently limited by the constraints of existing quantum hardware. This research contributes to the emerging field of QML by examining its potential advantages and future implications in addressing complex data processing challenges.