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.

翻译：量子启发机器学习（QiML）是一个新兴领域，因其在经典计算框架中利用量子力学原理的潜力而受到全球研究者的关注。然而，当前的综述文献往往局限于对QiML的肤浅探讨，而将重点放在更广泛的量子机器学习（QML）领域。针对这一空白，本综述对QiML进行了全面整合的审视，深入探索其多元研究领域，包括张量网络模拟、去量子化算法等，展示了最新进展、实际应用，并揭示了潜在的研究方向。此外，通过分析该术语的多种先前解释及其固有的模糊性，确立了QiML的具体定义。随着QiML的持续发展，我们预计未来将从量子力学、量子计算和经典机器学习中涌现大量成果，进一步丰富该领域。本综述旨在为研究人员和实践者提供指导，帮助其全面理解QiML的当前格局与未来方向。