aiqbench analysis
This research introduces RASP-QAOA, a resource-aware selector that optimizes exact simulations for the Quantum Approximate Optimization Algorithm by dynamically choosing computational representations based on graph structure and circuit depth. By effectively matching execution requirements to specific backend capabilities, the method significantly reduces failure rates compared to static selection strategies in high-memory environments like H200 GPUs. The findings highlight critical optimization challenges at the intersection of quantum algorithm design and classical machine learning infrastructure management.
RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation
arXiv:2608.05646v1 Announce Type: cross 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.
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.
9 pages, 5 figures. Code and reproducibility package: this https URL
Emerging Technologies (cs.ET); Machine Learning (cs.LG)
arXiv:2608.05646 [cs.ET]
(or arXiv:2608.05646v1 [cs.ET] for this version)
https://doi.org/10.48550/arXiv.2608.05646 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 290 words
Related coverage
Entanglement-Capability-Aware Finite-Bond Circuit Cutting for Distributed Quantum Computing
arXiv · Oct 14, 2026
Adaptive Differential Evolution and Multistart Search for Noisy QAOA Optimization
arXiv · Sep 22, 2026
Run agent-driven Amazon SageMaker HyperPod operations with InstantStart
AWS · Sep 4, 2026
Problems of Proliferation
LessWrong · Sep 29, 2026