Financial Risk Management on a Neutral Atom Quantum Processor

• URL: http://arxiv.org/abs/2212.03223v2
• Date: Wed, 3 Apr 2024 10:04:02 GMT
• Title: Financial Risk Management on a Neutral Atom Quantum Processor
• Authors: Lucas Leclerc, Luis Ortiz-Guitierrez, Sebastian Grijalva, Boris Albrecht, Julia R. K. Cline, Vincent E. Elfving, Adrien Signoles, Loïc Henriet, Gianni Del Bimbo, Usman Ayub Sheikh, Maitree Shah, Luc Andrea, Faysal Ishtiaq, Andoni Duarte, Samuel Mugel, Irene Caceres, Michel Kurek, Roman Orus, Achraf Seddik, Oumaima Hammammi, Hacene Isselnane, Didier M'tamon,
• Abstract summary: We propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.

Related papers

• Exploring Quantum Neural Networks for Demand Forecasting [0.25128687379089687]
  This paper presents an approach for training demand prediction models using quantum neural networks. A classical recurrent neural network was used to compare the results. They show a similar predictive capacity between the classical and quantum models.
  arXiv  Detail & Related papers  (2024-10-19T13:01:31Z)
• Quantum Computational Algorithms for Derivative Pricing and Credit Risk in a Regime Switching Economy [0.0]
  We introduce a class of processes that are both realistic in terms of mimicking financial market risks as well as more amenable to potential quantum computational advantages. We study algorithms to estimate credit risk and option pricing on a gate-based quantum computer.
  arXiv  Detail & Related papers  (2023-11-01T20:15:59Z)
• Designing an attack-defense game: how to increase robustness of financial transaction models via a competition [69.08339915577206]
  Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical. We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input. We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data. The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
  arXiv  Detail & Related papers  (2023-08-22T12:53:09Z)
• Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models [0.0]
  The Quantum Support Vector model achieved the highest performance, with F1 scores of 0.98 0.98 for fraud and non-fraud classes. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection.
  arXiv  Detail & Related papers  (2023-08-09T21:47:50Z)
• Quantum Deep Hedging [10.243020478772056]
  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. We successfully implement the proposed models on a trapped-ion quantum processor.
  arXiv  Detail & Related papers  (2023-03-29T10:42:50Z)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• Towards practical Quantum Credit Risk Analysis [0.5735035463793008]
  CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup has been introduced. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations.
  arXiv  Detail & Related papers  (2022-12-14T09:25:30Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Hessian-based toolbox for reliable and interpretable machine learning in physics [58.720142291102135]
  We present a toolbox for interpretability and reliability, extrapolation of the model architecture. It provides a notion of the influence of the input data on the prediction at a given test point, an estimation of the uncertainty of the model predictions, and an agnostic score for the model predictions. Our work opens the road to the systematic use of interpretability and reliability methods in ML applied to physics and, more generally, science.
  arXiv  Detail & Related papers  (2021-08-04T16:32:59Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.