Abstract

Quantum computing represents a paradigm shift in computational theory, offering unprecedented capabilities for solving complex problems that are intractable for classical computers. The foundational concepts are quantum mechanics, including superposition, entanglement, and quantum gates, these principles form the basis of quantum computation. Quantum bits, or qubits, leverage superposition and entanglement to perform calculations in a fundamentally different manner than classical bits, allowing for exponential parallelism and potentially revolutionary computational power. Quantum machine learning (QML) stands at the nexus of two cutting-edge fields: quantum computing and data science. Combining the principles of quantum mechanics with the computational power of quantum computers, QML aims to harness quantum phenomena to enhance traditional machine learning algorithms. Quantum machine learning algorithms leverage quantum states, such as superposition and entanglement, to process and analyse data in ways that surpass the capabilities of classical methods. Quantum machine learning represents a frontier in both quantum computing and data science, offering novel approaches to data analysis and decision-making with the potential to transform industries and drive scientific discovery.