In this paper, we introduce a quantum-enhanced algorithm for simulation-based optimization. Simulation-based optimization seeks to optimize an objective function that is computationally expensive to evaluate exactly, and thus, is approximated via simulation. Quantum Amplitude Estimation (QAE) can achieve a quadratic speed-up over classical Monte Carlo simulation. Hence, in many cases, it can achieve a speed-up for simulation-based optimization as well. Combining QAE with ideas from quantum optimization, we show how this can be used not only for continuous but also for discrete optimization problems. Furthermore, the algorithm is demonstrated on illustrative problems such as portfolio optimization with a Value at Risk constraint and inventory management.
Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area（粤港澳大湾区量子科学中心）；Department of Physics, Southern University of Science and Technology（南方科技大学物理系）
Quantum-enhanced Markov Chain Monte Carlo for Combinatorial Optimization
IBM Quantum, IBM Research Europe – Hursley, United Kingdom（IBM量子，IBM欧洲研究-赫斯利）；IBM Quantum, IBM Research Europe – Zurich, Switzerland（IBM量子，IBM欧洲研究-苏黎世）；The Hartree Centre, STFC, Sci-Tech Daresbury, United Kingdom（哈特里中心，STFC，达尔斯伯里科技）；The Open Quantum Institute, Geneva, Switzerland（开放量子研究所）；IBM Quantum, IBM Research Europe – Böblingen, Germany（IBM量子，IBM欧洲研究-博布林根）
Diffusion-Enhanced Optimization of Variational Quantum Eigensolver for General Hamiltonians
School of Physics, Beihang University（北京航空航天大学物理学院）；School of Physics, Beijing Institute of Technology（北京理工大学物理学院）；School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University（北京信息科技大学仪器科学与光电工程学院）
Generative-enhanced optimization for knapsack problems: an industry-relevant study
BMW Group（宝马集团）；BASF Digital Solutions GmbH（巴斯夫数字解决方案有限公司）；Lufthansa Industry Solutions（汉莎工业解决方案公司）；Siemens AG（西门子股份公司）；SAP SE（思爱普公司）；Technical University of Munich（慕尼黑工业大学）；QUTAC, Quantum Technology and Application Consortium（QUTAC量子技术与应用联盟）
