# Improved Financial Forecasting via Quantum Machine Learning

arXiv: https://arxiv.org/abs/2306.12965 · HTML: https://mlsanity.com/paper/2306.12965/

## Key facts

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

## Builds on (most cited first, 18 of 18)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Unitary Evolution Recurrent Neural Networks](https://mlsanity.com/paper/1511.06464/) (2015)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [Orthogonal Deep Neural Networks](https://mlsanity.com/paper/1905.05929/) (2019)
- [Quantum Vision Transformers](https://mlsanity.com/paper/2209.08167/) (2022)
- [Determinantal Point Processes in Randomized Numerical Linear Algebra](https://mlsanity.com/paper/2005.03185/) (2020)
- [GDPP: Learning Diverse Generations Using Determinantal Point Process](https://mlsanity.com/paper/1812.00068/) (2018)
- [Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes](https://mlsanity.com/paper/1602.05242/) (2016)
- [Monte Carlo with Determinantal Point Processes](https://mlsanity.com/paper/1605.00361/) (2016)
- [Exact sampling of determinantal point processes with sublinear time preprocessing](https://mlsanity.com/paper/1905.13476/) (2019)
- [Sampling from a k-DPP without looking at all items](https://mlsanity.com/paper/2006.16947/) (2020)
- [Leveraged volume sampling for linear regression](https://mlsanity.com/paper/1802.06749/) (2018)
- [Quantum machine learning with subspace states](https://mlsanity.com/paper/2202.00054/) (2022)
- [Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling](https://mlsanity.com/paper/1608.01008/) (2016)
- [Quantum Algorithms for Portfolio Optimization](https://mlsanity.com/paper/1908.08040/) (2019)
- [Low depth algorithms for quantum amplitude estimation](https://mlsanity.com/paper/2012.03348/) (2020)
- [Financial Risk Management on a Neutral Atom Quantum Processor](https://mlsanity.com/paper/2212.03223/) (2022)

## Built on by (most cited first, 14 of 14)

- [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)
- [Training-efficient density quantum machine learning](https://mlsanity.com/paper/2405.20237/) (2024)
- [HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction](https://mlsanity.com/paper/2503.15403/) (2025)
- [QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting](https://mlsanity.com/paper/2408.12007/) (2024)
- [QuIC: Quantum-Inspired Compound Adapters for Parameter Efficient Fine-Tuning](https://mlsanity.com/paper/2502.06916/) (2025)
- [Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders](https://mlsanity.com/paper/2504.13113/) (2025)
- [Quantum autoencoders for image classification](https://mlsanity.com/paper/2502.15254/) (2025)
- [Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection](https://mlsanity.com/paper/2504.18103/) (2025)
- [Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis](https://mlsanity.com/paper/2502.07806/) (2025)
- [Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting](https://mlsanity.com/paper/2504.00068/) (2025)
- [Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting](https://mlsanity.com/paper/2511.17698/) (2025)
- [Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning](https://mlsanity.com/paper/2602.00048/) (2026)
- [Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning](https://mlsanity.com/paper/2602.02962/) (2026)
- [HQFS: Hybrid Quantum Classical Financial Security with VQC Forecasting, QUBO Annealing, and Audit-Ready Post-Quantum Signing](https://mlsanity.com/paper/2602.16976/) (2026)

## 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.