Determine whether NISQ variational algorithms can outperform best-in-class classical methods

Determine whether variational quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices—such as the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA)—can outperform state-of-the-art classical algorithms on the same problem instances in combinatorial optimization and machine learning.

Background

Variational quantum algorithms are a central NISQ-era strategy, but they face challenges such as barren plateaus and local traps, and their expressivity–simulatability trade-offs raise concerns about classical competitiveness.

The authors note incremental theoretical progress (e.g., proofs of parameter concentration and special-case advantages) but emphasize that, on the overall question of end-to-end superiority over classical methods, a firm answer is still lacking.