Quantum-Inspired Machine Learning: a Survey

• URL: http://arxiv.org/abs/2308.11269v2
• Date: Fri, 8 Sep 2023 07:28:27 GMT
• Title: Quantum-Inspired Machine Learning: a Survey
• Authors: Larry Huynh, Jin Hong, Ajmal Mian, Hajime Suzuki, Yanqiu Wu, Seyit Camtepe
• Abstract summary: Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers. This survey explores QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning.
• Score: 32.913044758923455
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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