---
title: Quantum Machine Learning for Option Pricing
url: https://www.ml-quant.com/papers/ssrn/4673569/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4673569
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4673569
featured: 2024-01-03
citations: unknown
topic: Derivatives & Volatility
---


# Quantum Machine Learning for Option Pricing

The paper discusses the potential of quantum machine learning as an efficient alternative to classical machine learning in financial risk management.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4673569
- Identifier: SSRN 4673569
- Released: 2021-09-14
- First featured: Quant Letter No. 31 (2024-01-03): https://www.ml-quant.com/issues/2024-01-03/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [A Hamiltonian Approach to Barrier Option Pricing Under Vasicek Model](https://www.ml-quant.com/papers/arxiv/2307.07103/): The paper uses the Hamiltonian approach from quantum theory to option pricing with fluctuating interest rates, deriving pricing kernels and option prices under a specific model.
- [Notes on the SWIFT method based on Shannon Wavelets for Option Pricing - Revisited](https://www.ml-quant.com/papers/arxiv/2401.01758/): The note reexamines the SWIFT method for pricing European options under models with a known characteristic function in 2023, discussing potential enhancements and pointing out some limitations of the method.
- [Robust option pricing with volatility term structure -- An empirical study for variance options](https://www.ml-quant.com/papers/arxiv/2312.09201/): The research examines the robust option pricing issue, discovering that adding more information does not enhance the robust pricing bounds, contrary to popular belief.
- [A Two-Step Longstaff Schwartz Monte Carlo Approach to Game Option Pricing](https://www.ml-quant.com/papers/arxiv/2401.08093/): The article suggests a two-step Longstaff Schwartz Monte Carlo method for pricing game options, which provides more reliable results than the original method.
- [A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models](https://www.ml-quant.com/papers/arxiv/2401.06740/): The paper introduces a deep learning method for pricing European basket options using Artificial Neural Networks and two methods for discretizing the integral operator, focusing on assets with jump-diffusion dynamics.
- [Data-driven Option Pricing](https://www.ml-quant.com/papers/arxiv/2401.11158/): A new data-driven option pricing method is suggested, using historical asset prices and deep learning to solve optimization problems, proving effective in numerical tests.
