Low-depth Clifford circuits approximately solve MaxCut

TL;DR

This paper presents ADAPT-Clifford, a quantum-inspired approximation algorithm for MaxCut that uses low-depth Clifford circuits, demonstrating superior performance over classical algorithms on various graph instances.

Contribution

The paper introduces ADAPT-Clifford, a novel algorithm leveraging Clifford circuits for MaxCut, bridging quantum-inspired methods with classical approximation techniques.

Findings

ADAPT-Clifford finds better cuts than Goemans-Williamson on small graphs.

The algorithm achieves a solution with ~94% of the Parisi value for SK model.

Runtime scales as O(N^2) for sparse and O(N^3) for dense graphs.

Abstract

We introduce a quantum-inspired approximation algorithm for MaxCut based on low-depth Clifford circuits. We start by showing that the solution unitaries found by the adaptive quantum approximation optimization algorithm (ADAPT-QAOA) for the MaxCut problem on weighted fully connected graphs are (almost) Clifford circuits. Motivated by this observation, we devise an approximation algorithm for MaxCut, \emph{ADAPT-Clifford}, that searches through the Clifford manifold by combining a minimal set of generating elements of the Clifford group. Our algorithm finds an approximate solution of MaxCut on an $N$-vertex graph by building a depth $O(N)$ Clifford circuit. The algorithm has runtime complexity $O(N^2)$ and $O(N^3)$ for sparse and dense graphs, respectively, and space complexity $O(N^2)$, with improved solution quality achieved at the expense of more demanding runtimes. We implement ADAPT-Clifford and characterize its performance on graphs with positive and signed weights. The case of signed weights is illustrated with the paradigmatic Sherrington-Kirkpatrick model, for which our algorithm finds solutions with ground-state mean energy density corresponding to $\sim94\%$ of the Parisi value in the thermodynamic limit. The case of positive weights is investigated by comparing the cut found by ADAPT-Clifford with the cut found with the Goemans-Williamson (GW) algorithm. For both sparse and dense instances we provide copious evidence that, up to hundreds of nodes, ADAPT-Clifford finds cuts of lower energy than GW.