Evaluating Quantum Amplitude Estimation for Pricing Multi-Asset Basket Options
arXiv:2509.09432 · doi:10.1109/QAI63978.2025.00076
Abstract
Accurate and efficient pricing of multi-asset basket options poses a significant challenge, especially when dealing with complex real-world data. In this work, we investigate the role of quantum-enhanced uncertainty modeling in financial pricing options on real-world data. Specifically, we use quantum amplitude estimation and analyze the impact of varying the number of uncertainty qubits while keeping the number of assets fixed, as well as the impact of varying the number of assets while keeping the number of uncertainty qubits fixed. To provide a comprehensive evaluation, we establish and validate a hybrid quantum-classical comparison framework, benchmarking quantum approaches against classical Monte Carlo simulations and Black-Scholes methods. Beyond simply computing option prices, we emphasize the trade-off between accuracy and computational resources, offering insights into the potential advantages and limitations of quantum approaches for different problem scales. Our results contribute to understanding the feasibility of quantum methods in finance and guide the optimal allocation of quantum resources in hybrid quantum-classical workflows.
References in corpus (11)
- Quantum computing for finance: overview and prospects
- Quantum Risk Analysis
- Option Pricing using Quantum Computers
- Financial Fraud Detection using Quantum Graph Neural Networks
- Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models
- QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
- QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction
- Quantum Clustering for Cybersecurity
- A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
- LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks
- Quantum Portfolio Optimization with Expert Analysis Evaluation