Quantum-Inspired Machine Learning: a Survey
It addresses a gap in review literature for researchers and practitioners interested in leveraging quantum principles in classical machine learning, though it is incremental as a survey paper.
This survey tackles the lack of comprehensive reviews on Quantum-Inspired Machine Learning (QiML) by providing an integrated examination of its research domains, recent advancements, and future directions, establishing a concrete definition to clarify prior ambiguities.
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.