mlsanityData to 14 Oct 2026

Quantum Machine Learning for Finance

arXiv 2109.04298 ↗First posted 9 Sep 2021quant-ph cs.LGcited by 7

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Abstract (arXiv)

Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term. This review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 38 of its 137 references that are in the map. First posted on arXiv 9 Sep 2021.

Builds on

38 of 137 references.

References · 38

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26 more
  1. 2020Recurrent Quantum Neural Networkscited by 80
  2. 2019The Born Supremacy: Quantum Advantage and Training of an Ising Born Machinecited by 76
  3. 2019Deep Reinforcement Learning for Tradingcited by 73
  4. 2020Trainability of Dissipative Perceptron-Based Quantum Neural Networkscited by 56
  5. 2018q-means: A quantum algorithm for unsupervised machine learningcited by 55
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  7. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  8. 2017Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machinescited by 33
  9. 2018The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulationcited by 30
  10. 2016Quantum Perceptron Modelscited by 28
  11. 2016Quantum Algorithms for Compositional Natural Language Processingcited by 24
  12. 2019Experimental kernel-based quantum machine learning in finite feature spacecited by 21
  13. 2019A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolvercited by 20
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  15. 2020Training and Classification using a Restricted Boltzmann Machine on the D-Wave 2000Qcited by 18
  16. 2020Quantum Differentially Private Sparse Regression Learningcited by 18
  17. 2021Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Reviewcited by 17
  18. 2015Solving the Optimal Trading Trajectory Problem Using a Quantum Annealercited by 12
  19. 2018Quantum-inspired sublinear classical algorithms for solving low-rank linear systemscited by 12
  20. 2020Coreset Clustering on Small Quantum Computerscited by 10
  21. 2018Clustering and Learning from Imbalanced Datacited by 7
  22. 2019A network approach to cartel detection in public auction marketscited by 5
  23. 2019Quantum algorithms for Second-Order Cone Programming and Support Vector Machinescited by 4
  24. 2013Analyzing Big Data with Dynamic Quantum Clusteringcited by 2
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  26. 2019Quantum-Assisted Clustering Algorithms for NISQ-Era Devicescited by 1

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 1; 2025: 3.

7 papers.

  1. 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
  2. 2025Contextual Quantum Neural Networks for Stock Price Predictioncited by 4
  3. 2025Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoderscited by 3
  4. 2022Quantum version of the k-NN classifier based on a quantum sorting algorithmcited by 1
  5. 2022Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reductioncited by 1
  6. 2024Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learningcited by 1
  7. 2025Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equationscited by 0