Quantum Machine Learning Techniques for High-Performance Pattern Recognition

Abstract

Quantum computing has emerged as a radical paradigm of computationally tasks which cannot be achieved through classical systems. Quantum Computer-Assisted Machine Learning (Quantum Machine Learning) The use of quantum mechanics and principles of artificial intelligence, such that the quantum mechanics principles of superposition, entanglement and tunnelling are used to improve pattern recognition. Big data dimensions, exponential growth feature space, optimization bottlenecks will not be effectively handled using currently available machine learning algorithms. It is anticipated that QML will deliver factors-of-four to exponential training and inference gains, especially on high-performance pattern recognition tasks in on-the-edge applications, including image processing, natural language processing, and cybersecurity. This is a review article of quantum machine learning (high-performance pattern recognition). It studies some of the underlying paradigms of quantum support vector machine (QSVMs), quantum k-means clustering, variational quantum circuits (VQCs) and quantum-classical deep learning systems. The benefits of QML are addressed in relation to scalability, generalization, and resilience of high dimensional data space. Such use cases include biomedical imaging, business fraud, and material discovery. The QML does have several opportunities, but is restricted by noisy intermediate-scale quantum (NISQ) machines and error correction costs, and cannot be as easily co-located with classical pipes. Another objective of this paper is to discover the significance of the hybrid quantum-classical models that are the most realistic line of attack today, to the application of quantum techniques. The next line of research is hardware-efficient algorithms, quantum feature maps, and practical benchmarking of QML. The quantum computing-artificial intelligence interface can be the new frontier to highly-performing, scaled-out, and efficient pattern recognition systems in data-focused industries, QML may become the future of computation.