Feasibility and optimum recovery in warm-start quantum optimization for a drug-response model on a trapped-ion processor

2026-09-19

AI Summary

研究使用量子近似优化算法（QAOA）解决药物响应模型中的优化问题，但效果不佳。贪心搜索和退火方法表现更好。Grover混合器在保持可行性的同时提高了最优解概率。

Abstract

On a seven-compound drug-response model, warm-start quantum approximate optimization (QAOA) on IonQ Forte-1 returned valid assignments more often than random bitstrings, but this alone did not show effective optimization. Ideal QAOA raised optimum probability above uniform feasible sampling in only four of twelve reference circuits. Hardware often fell below its own noiseless circuits, while greedy search solved all hardware models within 200 objective evaluations. Expanded simulations showed a gain over feasible sampling in 28 of 35 CAMA-1 panels and none of four 647-V panels. Annealing solved all these panels in every seed. A Grover mixer preserved feasibility and improved optimum probability over feasible sampling in all ten tested models. We analyzed 25 completed tasks containing 5,300 shots from 18 circuits and 14 instances. The encodings use 6-35 qubits and at most 4,900 feasible assignments, which we enumerated to establish exact optima. Noiseless references now cover both the original six circuits and six wider circuits. At 35 qubits, with 4,900 feasible assignments, ideal feasibility was 35.85%, compared with 7 of 200 valid hardware outputs. Its ideal optimum probability was below both sampling controls. The circuits sample assignments in a model built from measured single-agent and pairwise responses.

Problem

• quantum optimization
• drug-response model
• optimum probability
• hardware noise
• feasibility

Innovation

• Grover mixer
• feasibility preservation
• optimum probability improvement
• quantum approximate optimization algorithm (QAOA)
• drug-response model