Variational Quantum Algorithms and Quantum Machine Learning

Joseph Lindsay, Ramtin Zand

Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent work, many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically small enough to be compatible with NISQ devices and the method of automatic differentiation for optimizing circuit parameters is familiar to machine learning (ML). While the results bare interesting promise for an era when quantum machines are more readily accessible, if one can achieve similar results through non-quantum methods then there may be a more near-term advantage available to practitioners. To this end, the nature of this work is to investigate the utilization of stochastic methods inspired by a variational quantum version of the long short-term memory (LSTM) model in attempt to approach the reported successes in performance and rapid convergence. By analyzing the performance of classical, stochastic, and quantum methods, this work aims to elucidate if it is possible to achieve some of QML’s major reported benefits on classical machines by incorporating aspects of its stochasticity.

Alessandro Luongo

Marco Cerezo, Andrew Arrasmith, Ryan Babbush +8 more

Quoc Chuong Nguyen, Le Bin Ho, Lan Nguyen Tran +1 more

Abstract Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms (VQAs) have been proposed and tested on QVMs to surpass the limitations of quantum hardware. Our goal is to exploit further the VQAs towards practical applications of quantum machine learning (QML) using state-of-the-art quantum computers. In this paper, we first introduce a QVM named Qsun, whose operation is underlined by quantum state wavefunctions. The platform provides native tools supporting VQAs. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of QML: quantum linear regression and quantum neural network.

Tessa E. Livingston, Suresh Muknahallipatna

Quantum computing, in the last few years, has made tremendous gains both scientifically and commercially. The recent announcements by Google and Microsoft on new qubit implementation approaches, addressing scaling and error correction, forecast the superior capability of quantum computing over classical computing in various application domains. One area that has seen significant advancements in research is the application of quantum algorithms in machine learning, giving rise to the exciting field of quantum machine learning. Therefore, the essence of this paper is to survey the recent advancements in quantum machine learning. The survey primarily focuses on hybrid quantum machine learning, with a particular emphasis on recent research advancements in the application of variational quantum algorithms and data encoding for quantum machine learning. The paper discusses the use of variational quantum algorithms in classification and regression tasks. In classification, recent work in image, speech, and text classification is discussed, highlighting the advances and limitations. In the case of regression, recent research work in single and multi-dimensional regressions is discussed. This survey aims to cover the application of variational quantum algorithms in enhancing the capabilities of hybrid quantum machine learning.

Antonio Macaluso, L. Clissa, Stefano Lodi +1 more