Scaling Advantage in Approximate Optimization with Quantum Annealing

TL;DR

This paper demonstrates a scaling advantage of quantum annealing over classical heuristics in approximate optimization, using error correction techniques on a quantum annealer to solve high-precision spin-glass problems more efficiently.

Contribution

The paper provides the first evidence of an algorithmic quantum speedup in approximate optimization by implementing quantum annealing correction on a large-scale quantum annealer.

Findings

Quantum annealing with QAC outperforms classical algorithms in scaling for low-energy state sampling.

Over 1,300 error-suppressed logical qubits achieved on a degree-5 interaction graph.

First demonstration of a quantum speedup in approximate optimization.

Abstract

Quantum annealing is a heuristic optimization algorithm that exploits quantum evolution to approximately find lowest energy states. Quantum annealers have scaled up in recent years to tackle increasingly larger and more highly connected discrete optimization and quantum simulation problems. Nevertheless, despite numerous attempts, a computational quantum advantage in exact optimization using quantum annealing hardware has so far remained elusive. Here, we present evidence for a quantum annealing scaling advantage in approximate optimization. The advantage is relative to the top classical heuristic algorithm: parallel tempering with isoenergetic cluster moves (PT-ICM). The setting is a family of 2D spin-glass problems with high-precision spin-spin interactions. To achieve this advantage, we implement quantum annealing correction (QAC): an embedding of a bit-flip error-correcting code with energy penalties that leverages the properties of the D-Wave Advantage quantum annealer to yield over 1,300 error-suppressed logical qubits on a degree-5 interaction graph. We generate random spin-glass instances on this graph and benchmark their time-to-epsilon, a generalization of the time-to-solution metric for low-energy states. We demonstrate that with QAC, quantum annealing exhibits a scaling advantage over PT-ICM at sampling low energy states with an optimality gap of at least 1.0%. This amounts to the first demonstration of an algorithmic quantum speedup in approximate optimization.