Improving the performance of quantum algorithms is a fundamental task to achieve quantum advantage. In many cases, extracting information from quantum systems poses an important challenge for practical implementations in real-world quantum computers, given the high resource cost of performing state tomography. In this scenario, randomized measurements emerged as a promising tool. In particular, the classical shadows protocol allows one to retrieve expected values of low-weight Pauli observables by performing only local measurements. In this paper, we present an implementation of the recently introduced Feedback-based algorithm for quantum optimization (FALQON) with the Ket quantum programming platform, for solving the MaxCut optimization problem. We employ classical shadows for the feedback routine of parameter estimation and compare this approach with the direct estimation of observables. Our results show that depending on the graph geometry for the MaxCut problem, the number of measurements required to estimate expected values of observables with classical shadows can be up to 16 times lower than with direct observable estimation. Furthermore, by analyzing complete graphs, we numerically confirm a logarithmic growth in the required number of measurements relative to the number of observables, reinforcing that classical shadows can be a useful tool for estimating low-locality Pauli observables in quantum algorithms.
Feedback-based quantum optimization and its classical counterpart: quantum advantage and the power of classical algorithms
Graduate School of Science and Technology, Keio University（庆应义塾大学理工学研究科）；Basic Research Laboratories, NTT, Inc.（日本电报电话公司基础研究所）；NTT Research Center for Theoretical Quantum Information, NTT, Inc.（日本电报电话公司理论量子信息研究中心）
Accelerating Feedback-based Algorithms for Quantum Optimization Using Gradient Descent
Luddy School of Informatics, Computing, and Engineering（信息学、计算与工程学院）；Indiana University Bloomington（印第安纳大学布卢明顿分校）；Bloomington, IN, United States（布卢明顿，印第安纳州，美国）
Shadow tomography for classical tensor network simulations
Marcus Center for Theoretical Chemistry, California Institute of Technology（加州理工学院马库斯理论化学中心）；Division of Chemistry and Chemical Engineering, California Institute of Technology（加州理工学院化学与化学工程系）
