mlsanityData to 7 Oct 2026

Energy Accuracy Is Not Enough: A Structure-Aware Benchmark and Evaluation Protocol for Quantum Architecture Search

arXiv 2607.04845 ↗First posted 6 Jul 2026quant-ph cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Quantum architecture search for molecular ground-state estimation is commonly evaluated through energy accuracy, which does not describe circuit cost or the physical properties of the prepared state. We introduce HamQASBench, a structure-aware benchmark comprising eleven molecular Hamiltonians of up to fourteen qubits, selected using Hamiltonian and target-state properties and supplied with exact references. Its evaluation protocol combines energy accuracy and success rates with reference-relative circuit cost, local entropy profiles for non-degenerate targets, and state identification within degenerate ground subspaces. Experiments with five methods spanning four search paradigms reveal differences hidden by energy-only comparisons. On a near-product instance under the shallow search budget, the best outputs of all five methods reach chemical accuracy while using between two and sixty-two gates. Equal-energy outputs on a degenerate instance occupy distinct spin components. Local entropy profiles distinguish inaccurate outputs and show that entangling-gate counts need not reflect realized entanglement. Across the molecular instance ladder, product-state outputs can meet or miss chemical accuracy, motivating interpretation of success alongside target-state properties. These results support retaining energy as the task-success criterion while using circuit-cost and state diagnostics for more informative comparisons. The benchmark instances, references, evaluation implementation, and per-run data are released for reuse.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 35 references that are in the map. First posted on arXiv 6 Jul 2026.

Builds on

10 of 35 references.

References · 10

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  2. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 412
  3. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 340
  4. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 76
  5. 2020NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processingcited by 60
  6. 2024Curriculum reinforcement learning for quantum architecture search under hardware errorscited by 37
  7. 2022NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easycited by 36
  8. 2023Quantum circuit synthesis with diffusion modelscited by 33
  9. 2022Automated Quantum Circuit Design with Nested Monte Carlo Tree Searchcited by 16
  10. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwarecited by 7

Built on by

Not cited yet.