# Quantum-Inspired Machine Learning: a Survey

arXiv: https://arxiv.org/abs/2308.11269 · HTML: https://mlsanity.com/paper/2308.11269/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 268 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [TensorLy: Tensor Learning in Python](https://mlsanity.com/paper/1610.09555/) (2016)
- [TensorFlow Quantum: A Software Framework for Quantum Machine Learning](https://mlsanity.com/paper/2003.02989/) (2020)
- [Quantum Recommendation Systems](https://mlsanity.com/paper/1603.08675/) (2016)
- [Foundations for Near-Term Quantum Natural Language Processing](https://mlsanity.com/paper/2012.03755/) (2020)
- [lambeq: An Efficient High-Level Python Library for Quantum NLP](https://mlsanity.com/paper/2110.04236/) (2021)
- [Quantum Self-Attention Neural Networks for Text Classification](https://mlsanity.com/paper/2205.05625/) (2022)
- [Quantum Natural Language Processing on Near-Term Quantum Computers](https://mlsanity.com/paper/2005.04147/) (2020)
- [Systematic Literature Review: Quantum Machine Learning and its applications](https://mlsanity.com/paper/2201.04093/) (2022)
- [Tensor Networks Meet Neural Networks: A Survey and Future Perspectives](https://mlsanity.com/paper/2302.09019/) (2023)
- [Expressive power of recurrent neural networks](https://mlsanity.com/paper/1711.00811/) (2017)
- [TensorNetwork for Machine Learning](https://mlsanity.com/paper/1906.06329/) (2019)
- [Anomaly Detection with Tensor Networks](https://mlsanity.com/paper/2006.02516/) (2020)
- [Quantum Algorithms for Compositional Natural Language Processing](https://mlsanity.com/paper/1608.01406/) (2016)
- [Advances in Quantum Deep Learning: An Overview](https://mlsanity.com/paper/2005.04316/) (2020)
- [Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension](https://mlsanity.com/paper/1811.04909/) (2018)
- [Quantum-inspired algorithms in practice](https://mlsanity.com/paper/1905.10415/) (2019)
- [Tensor networks for quantum machine learning](https://mlsanity.com/paper/2303.11735/) (2023)
- [Generative modeling with projected entangled-pair states](https://mlsanity.com/paper/2202.08177/) (2022)
- [Entanglement-guided architectures of machine learning by quantum tensor network](https://mlsanity.com/paper/1803.09111/) (2018)
- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [CNM: An Interpretable Complex-valued Network for Matching](https://mlsanity.com/paper/1904.05298/) (2019)
- [Quantum-inspired sublinear classical algorithms for solving low-rank linear systems](https://mlsanity.com/paper/1811.04852/) (2018)
- [Revisiting dequantization and quantum advantage in learning tasks](https://mlsanity.com/paper/2112.00811/) (2021)
- [PennyLane: Automatic differentiation of hybrid quantum-classical computations](https://mlsanity.com/paper/1811.04968/) (2018)
- [Quantum-Inspired Tensor Neural Networks for Partial Differential Equations](https://mlsanity.com/paper/2208.02235/) (2022)
- [Adaptive Learning of Tensor Network Structures](https://mlsanity.com/paper/2008.05437/) (2020)
- [Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data](https://mlsanity.com/paper/2202.10471/) (2022)
- [An Improved Classical Singular Value Transformation for Quantum Machine Learning](https://mlsanity.com/paper/2303.01492/) (2023)

## Built on by (most cited first, 12 of 12)

- [Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments](https://mlsanity.com/paper/2501.09394/) (2025)
- [You Only Measure Once: On Designing Single-Shot Quantum Machine Learning Models](https://mlsanity.com/paper/2509.20090/) (2025)
- [Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation](https://mlsanity.com/paper/2410.10713/) (2024)
- [Training-free Quantum-Inspired Image Edge Extraction Method](https://mlsanity.com/paper/2501.18929/) (2025)
- [Boosting Classification with Quantum-Inspired Augmentations](https://mlsanity.com/paper/2506.22241/) (2025)
- [Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization](https://mlsanity.com/paper/2508.16611/) (2025)
- [Quantum-based QoE Optimization in Advanced Cellular Networks: Integration and Cloud Gaming Use Case](https://mlsanity.com/paper/2509.01008/) (2025)
- [A Quantum-Inspired Algorithm for Solving Sudoku Puzzles and the MaxCut Problem](https://mlsanity.com/paper/2510.19835/) (2025)
- [QiVC-Net: Quantum-Inspired Variational Convolutional Network, with Application to Biosignal Classification](https://mlsanity.com/paper/2511.05730/) (2025)
- [QML-HCS: A Hypercausal Quantum Machine Learning Framework for Non-Stationary Environments](https://mlsanity.com/paper/2511.17624/) (2025)
- [Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods](https://mlsanity.com/paper/2605.18821/) (2026)
- [Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions](https://mlsanity.com/paper/2608.17135/) (2026)

## Abstract (arXiv)

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.