Quantum Architecture Search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms, yet existing benchmarks organize instances by molecular identity or qubit count -- criteria agnostic to Hamiltonian structure -- and rely solely on energy accuracy, which cannot detect structural failures such as over-parameterization on near-product ground states. We introduce HamQASBench, a Hamiltonian-informed diagnostic benchmark organizing 11 molecules into five structural tiers via fingerprints derived from the Pauli operator basis, computational basis representation, and ground-state entanglement. A post-hoc critical-structure extraction procedure identifies minimal circuits consistent with each tier's requirements, complementing energy-based evaluation with per-qubit entanglement analysis and pairwise state fidelity. Benchmarking five QAS methods across four paradigms reveals failure modes invisible to conventional metrics: over-parameterization in the minimalism regime, eigenstate commitment under degeneracy, a representation bottleneck in strongly correlated systems, topology-induced routing failure, and circuit search space growth as a scalability bottleneck.
An open dataset and accompanying code are presented for preparing Dicke states using a collision-based quantum protocol and the accompanying code reproduces the processed data and validation checks, providing a reusable benchmark for studying the trade-off between state-preparation fidelity, circuit cost, and hardware...
Duc-Kha Vu, M. Nguyen, Ş. Özdemir et al.· 0 citations
The quantum-selected configuration interaction identifies important determinantal basis functions through real-time evolution of a reference wavefunction and diagonalizing the Hamiltonian matrix in the resulting selected subspace. However, implementing the full electronic Hamiltonian on noisy quantum devices leads to r...
Suprava Sahoo, Abdul Kalam, Kenji Sugisaki et al.· 0 citations
This work employs QESEM---a characterization-based, unbiased quasi-probabilistic mitigation method---on IBM's Aachen quantum processor to compute the ground-state potential energy surface (PES) of the symmetrically-stretched water molecule, demonstrating that the characterization-based, unbiased error mitigation provid...
Renato Olarte Hernandez, Emanuele Rossi, S. Coriani et al.· 0 citations
FlowMeas is introduced, which uses a generative flow network to directly sample finite ensembles of shallow Clifford measurement circuits subject to a prescribed shot budget and hardware constraints, and establishes generative learning as a flexible and unified framework for quantum measurement design under practical r...
Jun Dai, O. Nahman-Lévesque, Guillaume Rabusseau et al.· 0 citations
This work shows that SQD performance can be strongly influenced by uncontrolled growth of the classical diagonalization subspace, and establishes measurement-basis engineering as a promising route to improving quantum sampling methods for electronic structure.
Connor van Rossum, J. Cohn, Sally Shrapnel et al.· 3 citations
Estimating quantum state properties is essential across a wide range of applications, from studying quantum many-body physics to benchmarking quantum hardware. We present Qurrium, a Python package built on Qiskit that implements randomized measurement protocols for estimating purity, second-order R\'{e}nyi entropy, exp...
Huai-Chun Chang, Teik-Hui Lee, Arthur J. L. Strauss et al.· 0 citations
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