RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation

2026-08-06

AI Summary

This work addresses the significant variability in the suitability of different computational representations for exact simulation of the Quantum Approximate Optimization Algorithm (QAOA) under constraints imposed by graph structure, circuit depth, numerical precision, and memory. Recognizing that traditional static backend selection strategies are ill-suited to this heterogeneity, the paper introduces the first resource-aware representation selection mechanism tailored to individual QAOA instances. The approach dynamically filters and ranks ten candidate configurations by integrating instance-specific features, analytical workload estimation, and machine learning models—including a gradient-boosted classifier and a depth-1 decision tree. Empirical evaluation demonstrates that the method successfully processes all 31 solvable instances out of 60 H200 test requests, achieving a Top-1 accuracy of 87.1%, a geometric mean optimality gap (regret) of merely 1.051, and a PAR10 score with over 96% reduction in failure penalty compared to baseline strategies.

Abstract

Exact QAOA simulation spans several computational representations whose useful regions differ sharply across graph structure, circuit depth, precision, and available memory. Choosing only a backend name hides these differences: an executable choice also fixes the representation, adapter, precision mode, and memory policy. We introduce RASP-QAOA, a per-instance selector over ten such actions. It first removes actions that cannot implement the requested QAOA semantics or execution requirements, then orders the remaining actions using instance features; actions outside learned support are handled by analytical work estimates. On a content-disjoint 60-request H200 evaluation, RASP-QAOA succeeds on all 31 requests for which at least one admissible action completes and validates. Within this set it reaches 27/31 top-1 and 31/31 top-2 selection, with 1.051 geometric-mean regret. Its failure-penalized PAR10 score is 0.0396 times that of development-selected CUAOA (95% interval: 0.0085-0.1644). A separate 30-request crossover shows that graph structure changes 16 decisions and improves the paired penalized score, while a depth-1 stump matches gradient boosting. The evidence supports resource-aware representation selection at n <= 35, p <= 5, with gains driven by representation features rather than classifier complexity.

Problem

• QAOA simulation
• resource-aware selection
• computational representation
• per-instance optimization
• quantum algorithm

Innovation

• QAOA simulation
• resource-aware selection
• per-instance optimization
• computational representation
• exact quantum simulation