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Quantum Techniques in Machine Learning (QTML conference 2023)

Vedran Dunjko

Published Jan 1, 2023• 0 citations
OpenAlex

Abstract

<!--HTML-->Abstract: Although still a relatively niche field in classical machine learning, topological data analysis has raised substantial interest from the perspective of quantum algorithms in the last few years. In this talk we will introduce the topic of topological data analysis, and discuss the state-of-art of quantum algorithms for this problem, together with their promises and limitations, possible generalisations and connections to many-body physics.

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Quantum-inspired machine learning aims to leverage the principles of quantum mechanics to enhance data analysis and pattern recognition. This paper introduces a novel framework, Quantum-Inspired Machine Learning (QIM), designed to mimic the structure of topological data analysis (TDA), enabling the identification of complex patterns and anomalies. We propose a 'topological density' metric as a core feature extraction method, driving the development of a machine learning model that explores the interconnectedness of data points. The core claim is that this approach offers a fundamentally different perspective on data, leading to the detection of hidden patterns and anomalies previously difficult to identify using traditional machine learning techniques. This research explores the potential of QIM to revolutionize data analysis by introducing a novel approach to feature representation and pattern detection.
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At the nexus of quantum computing and machine learning, quantum machine learning (QML) and quantum deep learning (QDL) have emerged as promising fields. By enhancing computational capabilities and addressing challenging data analysis problems, these approaches have the potential to revolutionise a number of domains by leveraging the special properties of quantum systems, such as superposition and entanglement. In this article, we give a thorough analysis of QML and QDL algorithms, discussing their underlying ideas, benefits over conventional models, and potential uses. We talk about the difficulties these algorithms face, such as noisy quantum hardware, constrained qubit resources, training and optimisation issues, input representation and data encoding problems, and limited interpretability. Additionally, we look at current developments and lines of inquiry that aim to address these issues, including hybrid classical-quantum methods, error-mitigating techniques, better data representation techniques, quantum transfer learning, and resource optimisation.