mlsanityData to 7 Oct 2026

Improved Financial Forecasting via Quantum Machine Learning

arXiv 2306.12965 ↗First posted 31 May 2023q-fin.ST cs.LG quant-phcited by 14

Not on the map. Why?

Abstract (arXiv)

Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 18 of its 53 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

18 of 53 references.

References · 18

  1. 2018Supervised learning with quantum enhanced feature spacescited by 654
  2. 2019Parameterized quantum circuits as machine learning modelscited by 330
  3. 2015Unitary Evolution Recurrent Neural Networkscited by 291
  4. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 270
  5. 2019Orthogonal Deep Neural Networkscited by 72
  6. 2022Quantum Vision Transformerscited by 48
  7. 2020Determinantal Point Processes in Randomized Numerical Linear Algebracited by 41
  8. 2018GDPP: Learning Diverse Generations Using Determinantal Point Processcited by 26
  9. 2016Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processescited by 24
  10. 2016Monte Carlo with Determinantal Point Processescited by 22
  11. 2019Exact sampling of determinantal point processes with sublinear time preprocessingcited by 19
  12. 2020Sampling from a k-DPP without looking at all itemscited by 17
6 more
  1. 2018Leveraged volume sampling for linear regressioncited by 15
  2. 2022Quantum machine learning with subspace statescited by 11
  3. 2016Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Samplingcited by 8
  4. 2019Quantum Algorithms for Portfolio Optimizationcited by 8
  5. 2020Low depth algorithms for quantum amplitude estimationcited by 4
  6. 2022Financial Risk Management on a Neutral Atom Quantum Processorcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 8; 2026: 3.

14 papers.

Sort:

  1. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  2. 2024Training-efficient density quantum machine learningcited by 10
  3. 2025HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Predictioncited by 10
  4. 2024QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecastingcited by 5
  5. 2025QuIC: Quantum-Inspired Compound Adapters for Parameter Efficient Fine-Tuningcited by 4
  6. 2025Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoderscited by 3
  7. 2025Quantum autoencoders for image classificationcited by 2
  8. 2025Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysiscited by 1
  9. 2025Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecastingcited by 1
  10. 2025Bayesian Quantum Orthogonal Neural Networks for Anomaly Detectioncited by 1
  11. 2025Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecastingcited by 1
  12. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
2 more
  1. 2026Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learningcited by 1
  2. 2026HQFS: Hybrid Quantum Classical Financial Security with VQC Forecasting, QUBO Annealing, and Audit-Ready Post-Quantum Signingcited by 0