Physics-Informed Bayesian Optimization of Variational Quantum Circuits
University of Bonn（波恩大学）；Helmholtz Institute for Radiation and Nuclear Physics (HISKP)（亥姆霍兹辐射与核物理研究所）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所）；Northeastern University London（东北大学伦敦分校）；Khoury College of Computer Sciences, Northeastern University（东北大学卡尔利计算机科学学院）；Deutsches Elektronen-Synchrotron (DESY)（德国电子同步加速器研究所）；Institute of Photonic Sciences, The Barcelona Institute of Science and Technology (ICFO)（光子科学研究所（巴塞罗那科学技术学院））；Prescient Design, gRED, Roche（罗氏公司gRED部门精准设计团队）；Technische Universität Berlin（柏林工业大学）；Max Planck Institut für Informatik（马克斯·普朗克信息学研究所）；RIKEN Center（理化学研究所中心）
Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima
在本文中，我们提出了一种新颖且强大的方法，将贝叶斯优化用于变分量子本征求解器（Variational Quantum Eigensolvers, VQEs）——一种用于近似量子哈密顿量基态的混合量子-经典协议。具体来说，我们推导出一个VQE核，该核融入了关于量子电路的重要先验信息：VQE核的核特征映射与VQE目标函数的已知函数形式精确匹配，从而显著降低了后验不确定性。此外，我们提出了一种新的贝叶斯优化采集函数，称为置信区域上的期望最大改进（Expected Maximum Improvement over Confident Regions, EMICoRe），该函数能够主动利用VQE核的归纳偏置，将预测不确定性较低的区域视为间接“已观测”。结果表明，在搜索域中仅需少至三个点的观测，就足以确定优化景观中整个一维子空间上的完整目标函数。我们的数值实验表明，该方法优于当前最先进的基线方法。
英文摘要：
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.
Sub-universal variational circuits for combinatorial optimization problems
Department of Physics, University of Colorado Boulder, Boulder, CO 80309, USA（科罗拉多大学博尔德分校物理系）；University of Technology Sydney, Centre for Quantum Software（悉尼技术大学量子软件中心）；Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, USA（科罗拉多大学博尔德分校电气、计算机与能源工程系）
Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Selangor, Malaysia（计算机科学与多媒体学院，林肯大学学院，Petaling Jaya，Selangor，马来西亚）
Universal Effectiveness of High-Depth Circuits in Variational Eigenproblems
Institute for Advanced Study（高等研究院）；Samsung Research（三星研究院）；Korea Institute for Advanced Study（韩国高等研究院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
Image Classification with Rotation-Invariant Variational Quantum Circuits
Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA)（伊克兰技术研究中心、巴斯克研究与技术联盟）；Donostia International Physics Center（多诺西亚国际物理中心）
INI-VPINN: A Variational Physics-Informed Neural Network with Implicit Neumann and Interface Handling for Multi-Material Domains with Geometric Singularities
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
University of California, Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；St. Kliment Ohridski University of Sofia（索非亚圣克里门特·奥赫里德大学）；Max Planck Institute for the Physics of Complex Systems（马克斯·普朗克复杂系统物理研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea（延世大学集成技术学院）；Department of Quantum Information, Yonsei University, Incheon 21983, Republic of Korea（延世大学量子信息系）
Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
Department of Civil Engineering and Geosciences（土木工程与地球科学系）；Technical University of Delft（代尔夫特理工大学）；Department of Engineering（工程系）；University of Cambridge（剑桥大学）
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
University of Cambridge（剑桥大学）；Imperial College London（伦敦帝国理工学院）；London School of Economics and Political Science（伦敦政治经济学院）；The Alan Turing Institute（阿兰·图灵研究所）
