TY  - CONF
AU  - Nicoli, Kim A.
AU  - Anders, Christopher J.
AU  - Funcke, Lena
AU  - Hartung, Tobias
AU  - Jansen, Karl
AU  - Kühn, Stefan
AU  - Müller, Klaus-Robert
AU  - Stornati, Paolo
AU  - Kessel, Pan
AU  - Nakajima, Shinichi
TI  - Physics-Informed Bayesian Optimization of Variational Quantum Circuits
IS  - arXiv:2406.06150
M1  - PUBDB-2024-07802
M1  - arXiv:2406.06150
SP  - 36
PY  - 2024
N1  - 36 pages, 17 figures, 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
AB  - 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.
T2  - 37th Conference on Neural Information Processing Systems
CY  - 10 Dec 2023 - 16 Dec 2023, New Orleans (United States)
Y2  - 10 Dec 2023 - 16 Dec 2023
M2  - New Orleans, United States
KW  - optimization: variational (INSPIRE)
KW  - dimension: 1 (INSPIRE)
KW  - quantum circuit: variational (INSPIRE)
KW  - variational quantum eigensolver (INSPIRE)
KW  - Bayesian (INSPIRE)
KW  - Hamiltonian (INSPIRE)
KW  - ground state (INSPIRE)
KW  - hybrid (INSPIRE)
KW  - landscape (INSPIRE)
LB  - PUB:(DE-HGF)8
DO  - DOI:10.3204/PUBDB-2024-07802
UR  - https://bib-pubdb1.desy.de/record/619663
ER  -