Energy Accuracy Is Not Enough: A Structure-Aware Benchmark and Evaluation Protocol for Quantum Architecture Search
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
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
- 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 412
- 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 340
- 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 76
- 2020NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processingcited by 60
- 2024Curriculum reinforcement learning for quantum architecture search under hardware errorscited by 37
- 2022NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easycited by 36
- 2023Quantum circuit synthesis with diffusion modelscited by 33
- 2022Automated Quantum Circuit Design with Nested Monte Carlo Tree Searchcited by 16
- 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwarecited by 7
Built on by
Not cited yet.