Quantum-Inspired Machine Learning: a Survey
Abstract
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues. Further, a concrete definition of QiML is established by analyzing various prior interpretations of the term and their inherent ambiguities. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning, enriching the field further. This survey serves as a guide for researchers and practitioners alike, providing a holistic understanding of QiML's current landscape and future directions.
Cite
@article{arxiv.2308.11269,
title = {Quantum-Inspired Machine Learning: a Survey},
author = {Larry Huynh and Jin Hong and Ajmal Mian and Hajime Suzuki and Yanqiu Wu and Seyit Camtepe},
journal= {arXiv preprint arXiv:2308.11269},
year = {2023}
}
Comments
59 pages, 13 figures, 9 tables. - Edited for spelling, grammar, and corrected minor typos in formulas - Adjusted wording in places for better clarity - Corrected contact info - Added Table 1 to clarify variables used in dequantized algs. - Added subsections in QVAS discussing QCBMs and TN-based VQC models - Included additional references as requested by authors to ensure a more exhaustive survey