Variational Quantum Algorithms (VQAs) have gained significant attention as a potential solution for various quantum computing applications in the near term. However, implementing these algorithms on quantum devices often necessitates a substantial number of measurements, resulting in time-consuming and resource-intensive processes. This paper presents a generalized framework for optimization algorithms aiming to reduce the number of shot evaluations in VQAs. The proposed framework combines an estimator and an optimizer. We investigate two specific case studies within this framework. In the first case, we pair a sample mean estimator with a simulated annealing optimizer, while in the second case, we combine a recursive estimator with a gradient descent optimizer. In both instances, we demonstrate that our proposed approach yields notable performance enhancements compared to conventional methods.
Numerical Optimization Strategies for the Variational Hamiltonian Ansatz in Noisy Quantum Environments
Gran Sasso Science Institute（格兰萨索科学研究所）；VSB-Technical University of Ostrava（俄斯特拉发理工大学）；IT4Innovations National Supercomputing Center, VSB–Technical University of Ostrava（俄斯特拉发理工大学 IT4创新国家超算中心）；Technical University of Munich（慕尼黑工业大学）；Fraunhofer Institute for Chemical Technology ICT（弗劳恩霍夫化学技术研究所）；Saarland University（萨尔兰大学）；University of L’Aquila（拉奎拉大学）
