Quantum Machine Learning Algorithms: Harnessing Quantum Supremacy for Data Processing

Abstract

Quantum Machine Learning (QML) is a new interdisciplinary area that applies quantum computing to audio and visual problems (or tasks) in order to solve them more efficiently than classical counterparts. On the other hand, traditional machine learning algorithms failed to compute an acceptable computation time due to the increasing complexity of real-world data. Quantum computing - based on the principles of quantum mechanics - like superposition, entanglement and quantum parallelism - promises exponential speed-ups for particular task types and therefore phenomenal progress in handling data and recognising trends. Abstract Recent advances towards quantum supremacy are likely to impact not only quantum computing for conventional problems, but also quantum machine learning algorithms in an attempt to access and benefit from any potential application of quantum supremacy. Quantum supremacy refers to when a quantum computer is able to perform a task which would take classical supercomputers, no matter how powerful, an practically incalculable amount of time or resources. It thus becomes relevant to understand how such machines can implement approaches such as hybrid quantum-classical algorithms, e.g., the Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm, that are also amenable to adaptation to machine learning work in the near-term intermediate scale quantum device regime. They consist of quantum support vector machines, quantum neural networks and quantum principal component analysis which take advantage of the utility of quantum computation to operate high dimensional space vectors and complex data structures more effectively. The capability to work with exponentially large Hilbert spaces to work with and analyze data is one of the most powerful features of QML. A 300 qubit quantum computer can represent many more states than atoms in the observed universe at the same time and offers ultra-high dimensional data representation to classification, clustering, optimization etc. Moreover, with the design of good quantum data encoding schemes (e.g., amplitude encoding, angle encoding) information can be represented in quantum circuits efficiently and manipulated, which may result in machine learning algorithms that run better on data sets of large size than classical algorithms. © 2025 IEEE.