%0 Conference Paper
%A Nicoli, Kim A.
%A Anders, Christopher J.
%A Funcke, Lena
%A Hartung, Tobias
%A Jansen, Karl
%A Kühn, Stefan
%A Müller, Klaus-Robert
%A Stornati, Paolo
%A Kessel, Pan
%A Nakajima, Shinichi
%T Physics-Informed Bayesian Optimization of Variational Quantum Circuits
%N arXiv:2406.06150
%M PUBDB-2024-07802
%M arXiv:2406.06150
%P 36
%D 2024
%Z 36 pages, 17 figures, 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
%X In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) - a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian. Specifically, we derive a VQE-kernel which incorporates important prior information about quantum circuits: the kernel feature map of the VQE-kernel exactly matches the known functional form of the VQE's objective function and thereby significantly reduces the posterior uncertainty. Moreover, we propose a novel acquisition function for Bayesian optimization called Expected Maximum Improvement over Confident Regions (EMICoRe) which can actively exploit the inductive bias of the VQE-kernel by treating regions with low predictive uncertainty as indirectly "observed". As a result, observations at as few as three points in the search domain are sufficient to determine the complete objective function along an entire one-dimensional subspace of the optimization landscape. Our numerical experiments demonstrate that our approach improves over state-of-the-art baselines.
%B 37th Conference on Neural Information Processing Systems
%C 10 Dec 2023 - 16 Dec 2023, New Orleans (United States)
Y2 10 Dec 2023 - 16 Dec 2023
M2 New Orleans, United States
%K optimization: variational (INSPIRE)
%K dimension: 1 (INSPIRE)
%K quantum circuit: variational (INSPIRE)
%K variational quantum eigensolver (INSPIRE)
%K Bayesian (INSPIRE)
%K Hamiltonian (INSPIRE)
%K ground state (INSPIRE)
%K hybrid (INSPIRE)
%K landscape (INSPIRE)
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%R 10.3204/PUBDB-2024-07802
%U https://bib-pubdb1.desy.de/record/619663