Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance
arXiv:2304.08793 · doi:10.1109/QCE57702.2023.00070
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.
arXiv comment10 pages, 12 figures, 2 algorithms
References in corpus (21)
- Quantum Computing in the NISQ era and beyond
- Quantum Machine Learning
- A variational eigenvalue solver on a quantum processor
- Variational Quantum Algorithms
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- The effect of data encoding on the expressive power of variational quantum machine learning models
- Quantum computing for finance: overview and prospects
- Quantum Risk Analysis
- Quantum computational finance: Monte Carlo pricing of financial derivatives
- Creating superpositions that correspond to efficiently integrable probability distributions
- Option Pricing using Quantum Computers
- Structure optimization for parameterized quantum circuits
- Quantum circuit architecture search for variational quantum algorithms
- A divide-and-conquer algorithm for quantum state preparation
- Differentiable Quantum Architecture Search
- A Threshold for Quantum Advantage in Derivative Pricing
- A Survey of Quantum Computing for Finance
- Robust resource-efficient quantum variational ansatz through evolutionary algorithm
- Quantum Architecture Search via Deep Reinforcement Learning
- Power and limitations of single-qubit native quantum neural networks
- A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond