Recently, there has been considerable interest in solving optimization problems by mapping these onto a binary representation, sparked mostly by the use of quantum annealing machines. Such binary representation is reminiscent of a discrete physical two-state system, such as the Ising model. As such, physics-inspired techniques -- commonly used in fundamental physics studies -- are ideally suited to solve optimization problems in a binary format. While binary representations can be often found for paradigmatic optimization problems, these typically result in k-local higher-order unconstrained binary optimization cost functions. In this work, we discuss the effects of locality reduction needed for the majority of the currently available quantum and quantum-inspired solvers that can only accommodate 2-local (quadratic) cost functions. General locality reduction approaches require the introduction of ancillary variables which cause an overhead over the native problem. Using a parallel tempering Monte Carlo solver on Microsoft Azure Quantum, as well as k-local binary problems with planted solutions, we show that post reduction to a corresponding 2-local representation the problems become considerably harder to solve. We further quantify the increase in computational hardness introduced by the reduction algorithm by measuring the variation of number of variables, statistics of the coefficient values, and the population annealing entropic family size. Our results demonstrate the importance of avoiding locality reduction when solving optimization problems.
CutVQA: Co-Designing Circuit Cutting and Architecture Search for Scaling Variational Quantum Algorithms
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Hefei National Laboratory, University of Science and Technology of China（中国科学技术大学合肥国家实验室）
Scalable Low-overhead Superconducting Non-local Coupler with Exponentially Enhanced Connectivity
Tsinghua University（清华大学）；Beijing Academy of Quantum Information Sciences（北京量子信息科学研究院）；Institute of Physics, Chinese Academy of Science（中国科学院物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hefei National Laboratory（合肥国家实验室）
QROB: Quantifying Realization Overhead in Quantum Compilation via Reverse Construction
Beijing Academy of Quantum Information Sciences（北京量子信息科学研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；Hefei National Laboratory（合肥国家实验室）
