Iterative warm-start optimization with quantum imaginary time evolution

TL;DR

This paper introduces a nonvariational quantum algorithm for combinatorial optimization that iteratively improves solutions using quantum imaginary time evolution, outperforming classical methods in simulations.

Contribution

It presents a novel warm-start, iterative quantum optimization approach based on quantum imaginary time evolution, evaluated through simulations on MaxCut problems.

Findings

Median solutions within 95% of the global optimum

Optimal solutions found in at least 11% of cases

Outperforms random and classical search procedures

Abstract

Approximate combinatorial optimization is a promising use case for quantum computers. The quantum optimization algorithms often employ a fixed ansatz that evolves an unbiased initial state towards states with better values of the optimand, then samples the states to determine an approximately optimal solution. However, promising alternative approaches have considered ``warm-start" and sampling-based methods that instead begin from the best known solution, which can be directly optimized with the quantum computer and updated as new information becomes available, potentially outperforming the fixed ans\"atze. Here we use these ideas to design a nonvariational quantum algorithm for combinatorial optimization. At each step the algorithm begins with a state superposed around the best known solution, then drives it to lower energy using quantum imaginary time evolution. These nonvariational, initial-state-dependent circuits are determined using analytic equations that are evaluated using only a conventional computer. After implementing the circuits, the state is sampled, potentially obtaining a new best-known solution to use as the initial state at the next iteration. Using simulations of the algorithm solving MaxCut on 3-regular graphs with 30 or fewer vertices and a shot budget of 100 total shots, the approach obtains median solutions within 95\% of the global optimum and finds optimal solutions in 11\% or more of cases, significantly outperforming random and simplified classical search procedures. We discuss several future directions.