mlsanityData to 7 Oct 2026

Quantum Deep Hedging

arXiv 2303.16585 ↗First posted 29 Mar 2023quant-ph cs.LG q-fin.CPcited by 22

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Abstract (arXiv)

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to 16 qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 39 of its 87 references that are in the map. First posted on arXiv 29 Mar 2023.

Builds on

39 of 87 references.

References · 39

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017A Distributional Perspective on Reinforcement Learningcited by 743
  3. 2020Variational Quantum Algorithmscited by 687
  4. 2018Supervised learning with quantum enhanced feature spacescited by 654
  5. 2018Barren plateaus in quantum neural network training landscapescited by 624
  6. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  7. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 464
  8. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 328
  9. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 270
  10. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  11. 2018Deep Hedgingcited by 106
  12. 2021Theory of overparametrization in quantum neural networkscited by 100
27 more
  1. 2021Parametrized quantum policies for reinforcement learningcited by 87
  2. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
  3. 2019Orthogonal Deep Neural Networkscited by 72
  4. 2022Group-Invariant Quantum Machine Learningcited by 56
  5. 2022A Survey on Quantum Reinforcement Learningcited by 50
  6. 2022Quantum Vision Transformerscited by 48
  7. 2019A Comparative Analysis of Expected and Distributional Reinforcement Learningcited by 47
  8. 2022Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuitscited by 42
  9. 2019Input Redundancy for Parameterized Quantum Circuitscited by 33
  10. 2018The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulationcited by 30
  11. 2020Pseudo-dimension of quantum circuitscited by 30
  12. 2019Enhancing Explainability of Neural Networks through Architecture Constraintscited by 29
  13. 2019Deep Hedging: Learning to Simulate Equity Option Marketscited by 25
  14. 2021Quantum Algorithms for Reinforcement Learning with a Generative Modelcited by 24
  15. 2021Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementationcited by 23
  16. 2018Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learningcited by 19
  17. 2022Quantum Reinforcement Learning via Policy Iterationcited by 19
  18. 2021Variational inference with a quantum computercited by 18
  19. 2021Multi-Asset Spot and Option Market Simulationcited by 17
  20. 2022Quantum policy gradient algorithmscited by 15
  21. 2022A Convergence Theory for Over-parameterized Variational Quantum Eigensolverscited by 14
  22. 2022Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversionscited by 13
  23. 2021Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measurescited by 11
  24. 2022Quantum machine learning with subspace statescited by 11
  25. 2019Quantum Algorithms for Portfolio Optimizationcited by 8
  26. 2018Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Modelcited by 4
  27. 2022Financial Risk Management on a Neutral Atom Quantum Processorcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 6; 2026: 4.

22 papers.

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  1. 2024Barren Plateaus in Variational Quantum Computingcited by 130
  2. 2023Does provable absence of barren plateaus imply classical simulability?cited by 78
  3. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 33
  4. 2024On the relation between trainability and dequantization of variational quantum learning modelscited by 24
  5. 2023Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapescited by 18
  6. 2024Training-efficient density quantum machine learningcited by 10
  7. 2025A Primer on Quantum Machine Learningcited by 9
  8. 2024Trainability issues in quantum policy gradientscited by 4
  9. 2025QuIC: Quantum-Inspired Compound Adapters for Parameter Efficient Fine-Tuningcited by 4
  10. 2024Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Informationcited by 2
  11. 2025Distributional Adversarial Attacks and Training in Deep Hedgingcited by 2
  12. 2024Architectures and random properties of symplectic quantum circuitscited by 1
10 more
  1. 2024Exploring Quantum Neural Networks for Demand Forecastingcited by 1
  2. 2025Bayesian Quantum Orthogonal Neural Networks for Anomaly Detectioncited by 1
  3. 2025Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environmentscited by 1
  4. 2023Computing the gradients with respect to all parameters of a quantum neural network using a single circuitcited by 0
  5. 2024On Quantum Natural Policy Gradientscited by 0
  6. 2025Taming Barren Plateaus in Arbitrary Parameterized Quantum Circuits without Sacrificing Expressibilitycited by 0
  7. 2026Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimizationcited by 0
  8. 2026QnRL: Quantum-Native Reinforcement Learningcited by 0
  9. 2026Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchycited by 0
  10. 2026Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approachcited by 0