Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance
| Autor: | Wolf, Mark-Oliver, Ewen, Tom, Turkalj, Ivica |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | 2023 IEEE International Conference on Quantum Computing and Engineering (QCE23) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/QCE57702.2023.00070 |
| Popis: | Classical Monte Carlo algorithms can theoretically be sped up on a quantum computer by employing amplitude estimation (AE). To realize this, an efficient implementation of state-dependent functions is crucial. We develop a straightforward approach based on pretraining parameterized quantum circuits, and show how they can be transformed into their conditional variant, making them usable as a subroutine in an AE algorithm. To identify a suitable circuit, we propose a genetic optimization approach that combines variable ansatzes and data encoding. We apply our algorithm to the problem of pricing financial derivatives. At the expense of a costly pretraining process, this results in a quantum circuit implementing the derivatives' payoff function more efficiently than previously existing quantum algorithms. In particular, we compare the performance for European vanilla and basket options. Comment: 10 pages, 12 figures, 2 algorithms |
| Databáze: | arXiv |
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