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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