Runtime Quantum Advantage with Digital Quantum Optimization

TL;DR

This paper demonstrates that digital quantum optimization algorithms on current quantum hardware can outperform classical methods like simulated annealing and CPLEX in solving specific higher-order binary optimization problems, showing early signs of quantum advantage.

Contribution

The study provides experimental evidence that digitized counterdiabatic quantum algorithms achieve faster runtimes than classical algorithms on existing quantum hardware for certain complex optimization problems.

Findings

Quantum algorithms outperform classical methods in runtime for selected problems.

Performance gap widens as system size increases.

Quantum advantage observed without error correction.

Abstract

We demonstrate experimentally that the bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm on IBM's 156-qubit devices can outperform simulated annealing (SA) and CPLEX in time-to-approximate solutions for specific higher-order unconstrained binary optimization (HUBO) problems. We suitably select problem instances that are challenging for classical methods, running in fractions of minutes even with multicore processors. On the other hand, our counterdiabatic quantum algorithms obtain similar or better results in at most a few seconds on quantum hardware, achieving runtime quantum advantage. Our analysis reveals that the performance improvement becomes increasingly evident as the system size grows. Given the rapid progress in quantum hardware, we expect that this improvement will become even more pronounced, potentially leading to a quantum advantage of several orders of magnitude. Our results indicate that available digital quantum processors, when combined with specific-purpose quantum algorithms, exhibit a runtime quantum advantage even in the absence of quantum error correction.