Quantum Machine Learning—An Overview

Abstract

Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While there is still a long way to go in terms of developing quantum hardware with sufficient resources, quantum machine learning holds great potential in areas such as unsupervised learning and generative models. Moving forward, more efforts are needed to explore new quantum learning models that can leverage the power of quantum mechanics to overcome the limitations of classical machine learning.

Quantum Machine Learning: Past, Present, and Future

“…(The most notable method, the Support Vector Machine (SVM), performs regression and classification. SVM increases the dimension of data (with a maximum threshold) until it becomes linearly separable as shown in Figure 5 (Tychola et al, 2023) [7] . QSVM, the quantum counterpart of this method, could perform matrix inversions and exponentials much more efficiently.…”

Nanowires: Exponential speedup in quantum computing

“…However, as traditional algorithms may not be sufficient, it is necessary to develop novel quantum algorithms. Shor's approach is capable of efficiently factoring large integers by utilizing quantum parallelism [ 186 ]. QML finds applications in diverse domains such as image processing, computational biology, bioinformatics, particle physics, communication networks, and privacy protection.…”

Quantum Computing: Circuits, Algorithms, and Applications

“…It can be used for financial interest rate modeling [89]. It has also been applied to develop financial models, such as churn prediction and credit risk assessment, where it has demonstrated better performance compared to traditional methods [101]. Quantum computing offers significant benefits in terms of computational speed and accuracy, making it a valuable tool in the finance field [102].…”