Towards practical Quantum Credit Risk Analysis

• URL: http://arxiv.org/abs/2212.07125v2
• Date: Mon, 19 Dec 2022 22:32:22 GMT
• Title: Towards practical Quantum Credit Risk Analysis
• Authors: Emanuele Dri, Edoardo Giusto, Antonello Aita, Bartolomeo Montrucchio
• Abstract summary: 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.
• Score: 0.5735035463793008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, a CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup over classical analogous methods has been introduced. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations (according to business domain experts) of this approach. In particular, we describe a method to implement a more realistic and complex risk model for the default probability of each portfolio's asset, capable of taking into account multiple systemic risk factors. In addition, we present a solution to increase the flexibility of one of the model's inputs, the Loss Given Default, removing the constraint to use integer values. This specific improvement addresses the need to use real data coming from the financial sector in order to establish fair benchmarking protocols. Although these enhancements come at a cost in terms of circuit depth and width, they nevertheless show a path towards a more realistic software solution. Recent progress in quantum technology shows that eventually, the increase in the number and reliability of qubits will allow for useful results and meaningful scales for the financial sector, also on real quantum hardware, paving the way for a concrete quantum advantage in the field. The paper also describes experiments conducted on simulators to test the circuit proposed and contains an assessment of the scalability of the approach presented.

Related papers

• Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis [0.0]
  This study introduces a framework for credit risk assessment in the banking sector, combining quantum deep learning techniques with adaptive modeling for Row-Type Dependent Predictive Analysis (RTDPA) The proposed approach tailors predictive models to different loan categories, aiming to enhance the accuracy and efficiency of credit risk evaluation.
  arXiv  Detail & Related papers  (2025-02-06T10:57:18Z)
• Bayesian Quantum Amplitude Estimation [49.1574468325115]
  We introduce BAE, a noise-aware Bayesian algorithm for quantum amplitude estimation. We show that BAE achieves Heisenberg-limited estimation and benchmark it against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Quantum Risk Analysis of Financial Derivatives [0.3749861135832073]
  We introduce two quantum algorithms to compute the Value at Risk (VaR) and Conditional Value at Risk (CVaR) of financial derivatives using quantum computers.
  arXiv  Detail & Related papers  (2024-04-15T18:52:30Z)
• Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization [59.758009422067]
  We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. We propose a new uncertainty Bellman equation (UBE) whose solution converges to the true posterior variance over values. We introduce a general-purpose policy optimization algorithm, Q-Uncertainty Soft Actor-Critic (QU-SAC) that can be applied for either risk-seeking or risk-averse policy optimization.
  arXiv  Detail & Related papers  (2023-12-07T15:55:58Z)
• An introduction to financial option pricing on a qudit-based quantum computer [0.0]
  The financial sector is anticipated to be one of the first industries to benefit from the increased computational power of quantum computers. Financial mathematics, and derivative pricing, are not areas quantum physicists are traditionally trained in.
  arXiv  Detail & Related papers  (2023-11-09T17:31:11Z)
• 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)
• Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks [142.67349734180445]
  Existing algorithms that provide risk-awareness to deep neural networks are complex and ad-hoc. Here we present capsa, a framework for extending models with risk-awareness.
  arXiv  Detail & Related papers  (2023-08-01T02:07:47Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Quantum advantage for multi-option portfolio pricing and valuation adjustments [0.0]
  We study the problem of Credit Valuation Adjustments (CVAs) which have significant importance in the valuation of derivative portfolios. We propose quantum algorithms that accelerate statistical sampling processes to approximate the CVA under different measures of dispersion.
  arXiv  Detail & Related papers  (2022-03-09T18:14:54Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• Chance-Constrained Control with Lexicographic Deep Reinforcement Learning [77.34726150561087]
  This paper proposes a lexicographic Deep Reinforcement Learning (DeepRL)-based approach to chance-constrained Markov Decision Processes. A lexicographic version of the well-known DeepRL algorithm DQN is also proposed and validated via simulations.
  arXiv  Detail & Related papers  (2020-10-19T13:09:14Z)

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.