Quantum Machine Learning: Applications, Algorithms, and Hardware Challenges

Abstract

Quantum Machine Learning (QML) integrates quantum algorithms into machine learning programs to enhance and expedite classical machine learning techniques. It leverages quantum data and hybrid quantum-classical models, utilizing the principles of superposition and entanglement to handle complex joint probability distributions that would require exponential classical computational resources. QML aims to harness the unique properties of quantum computers for practical machine learning tasks, with the goal of achieving quantum advantage over classical approaches. QML finds applications in diverse fields such as chemical and quantum matter simulation, quantum control, quantum communication networks, and quantum metrology. Quantum algorithms can analyze quantum states and improve computational speed and data storage. Two main frameworks, quantum kernel methods and variational quantum algorithms, are widely used due to their implement ability on quantum hardware and capacity to work on general datasets. Quantum computers can solve linear algebraic problems, calculate eigenvectors and eigenvalues, and perform support vector algorithms at exponentially faster rates than classical computers. Despite its potential, QML faces significant challenges related to quantum hardware capabilities, including limited qubit connectivity, noise, coherence times, and errors in state preparation and measurement. Hardware constraints, compatibility issues, high computational and memory requirements, and the difficulty of quantum programming also pose obstacles. Overcoming these hardware and software limitations is crucial for realizing the full potential of quantum machine learning