Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance

• URL: http://arxiv.org/abs/2304.08793v2
• Date: Mon, 18 Sep 2023 16:22:03 GMT
• Title: Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance
• Authors: Mark-Oliver Wolf, Tom Ewen, Ivica Turkalj
• Abstract summary: Classical Monte Carlo algorithms can theoretically be sped up on a quantum computer by employing amplitude estimation (AE) We develop a straightforward approach based on pretraining parameterized quantum circuits. We show how they can be transformed into their conditional variant, making them usable as a subroutine in an AE algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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