Speaker
Description
This work investigates a hybrid quantum–classical enhancement of the Metropolis–Hastings algorithm within a variational Monte Carlo framework. Using a single-thread Monte Carlo approach, we estimate the ground-state energy of a small Hamiltonian system by computing the dominant eigenvalue of a symmetric $3\times3$ matrix as a proof of concept.
A quantum-enhanced acceptance mechanism, inspired by the quantum Metropolis–Hastings algorithm, is integrated into the classical sampling procedure. The performance of this hybrid method is compared to the classical algorithm in terms of convergence speed and estimator precision. Numerical simulations demonstrate that the quantum-enhanced approach achieves faster convergence and improved accuracy for the test system, despite being implemented on classical hardware.
These results highlight the potential of quantum-assisted sampling techniques to improve the efficiency of Monte Carlo methods and provide a pathway toward scalable quantum algorithms for high-dimensional quantum systems.
| Apply for student award at which level: | MSc |
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| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |