Classically optimal variational quantum algorithms

TL;DR

This paper introduces a hybrid approach combining classical pre-computation with variational quantum algorithms to improve MAXCUT solutions, achieving classical performance guarantees and outperforming low-depth QAOA.

Contribution

It proposes integrating classical pre-computation into VQAs, exemplified by the Spanning Tree QAOA for MAXCUT, to enhance solution quality and performance guarantees.

Findings

ST-QAOA matches classical approximation guarantees

Hybrid classical-quantum approach improves MAXCUT solutions

Outperforms low-depth QAOA in experiments

Abstract

Hybrid quantum-classical algorithms, such as variational quantum algorithms (VQA), are suitable for implementation on NISQ computers. In this Letter we expand an implicit step of VQAs: the classical pre-computation subroutine which can non-trivially use classical algorithms to simplify, transform, or specify problem instance-specific variational quantum circuits. In VQA there is a trade-off between quality of solution and difficulty of circuit construction and optimization. In one extreme, we find VQA for MAXCUT which are exact, but circuit design or variational optimization is NP-HARD. At the other extreme are low depth VQA, such as QAOA, with tractable circuit construction and optimization but poor approximation ratios. Combining these two we define the Spanning Tree QAOA (ST-QAOA) to solve MAXCUT, which uses an ansatz whose structure is derived from an approximate classical solution and achieves the same performance guarantee as the classical algorithm and hence can outperform QAOA at low depth. In general, we propose integrating these classical pre-computation subroutines into VQA to improve heuristic or guaranteed performance.