Quantum

QAOA Inside ADMM: Subproblem Wins Do Not Become Decomposition Wins at Matched Budgets

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 10/11/2026, 08:43:25 UTC.

Briefings

A new benchmark study compares quantum approximate optimization algorithm (QAOA) against classical solvers for mixed-binary problems under matched budgets. The study uses 23 optimizer evaluations per subproblem and finds that QAOA loses to random search at 12 and 16 binary variables, while winning when credited with its best sample. In a noiseless 12-qubit simulation, QAOA achieves 97.3% of planted-family subproblems, significantly outperforming local heuristics and semidefinite rounding.

Key points

• QAOA loses to random search at 12 and 16 binary variables under matched budgets.
• QAOA achieves 97.3% of planted-family subproblems in a noiseless 12-qubit simulation.

Context

This research benchmarks quantum approximate optimization algorithm (QAOA) as a binary subproblem solver in a two-block alternating direction method of multipliers (ADMM) for mixed-binary problems against classical solvers under matched budgets. The study uses 23 optimizer evaluations per subproblem and scoring determines the comparison.

Limitations

Results are from a single 12-qubit noiseless simulation; performance grows with larger exponents. No additional background context is supplied beyond the abstract.

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