تقرير
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
| العنوان: | Physics-Informed Bayesian Optimization of Variational Quantum Circuits |
|---|---|
| المؤلفون: | Nicoli, Kim A., Anders, Christopher J., Funcke, Lena, Hartung, Tobias, Jansen, Karl, Kühn, Stefan, Müller, Klaus-Robert, Stornati, Paolo, Kessel, Pan, Nakajima, Shinichi |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantum Physics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Quantum Physics |
| الوصف: | 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. Comment: 36 pages, 17 figures, 37th Conference on Neural Information Processing Systems (NeurIPS 2023) |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2406.06150Test |
| رقم الانضمام: | edsarx.2406.06150 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |