Computer Science Review· 2026Q1· Review
Quantum-inspired machine learning: a survey
- 9citations
- Q1SCImago
- 2026year
Short summary
This survey provides a comprehensive examination of Quantum-inspired Machine Learning (QiML), a field leveraging quantum mechanics principles in classical computing, by defining QiML, exploring its domains like tensor network simulations and dequantized algorithms, and highlighting advancements and future directions.
AI-generated from the title and abstract; the full text is not read.
Key points
- QiML leverages quantum mechanics principles within classical computational frameworks.
- The survey provides a concrete definition of QiML by analyzing prior interpretations and ambiguities.
- Key research domains explored include tensor network simulations and dequantized algorithms.
- The work highlights recent advancements, practical applications, and future research directions in QiML.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Computer Science Review, 2026 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science