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Enhancing Accuracy of Quantum-Selected Configuration Interaction Calculations using Multireference Perturbation Theory: Application to Aromatic Molecules

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arxiv 2503.22221 v1 pith:PCF5F5KY submitted 2025-03-28 physics.chem-ph quant-ph

classification physics.chem-phquant-ph
keywords calculationsqsciaccuracyconfigurationconfigurationsperturbationstatealgorithm
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Quantum-selected configuration interaction (QSCI) is a novel quantum-classical hybrid algorithm for quantum chemistry calculations. This method identifies electron configurations having large weights for the target state using quantum devices and allows CI calculations to be performed with the selected configurations on classical computers. In principle, the QSCI algorithm can take advantage of the ability to handle large configuration spaces while reducing the negative effects of noise on the calculated values. At present, QSCI calculations are limited by qubit noise during the input state preparation and measurement process, restricting them to small active spaces. These limitations make it difficult to perform calculations with quantitative accuracy. The present study demonstrates a computational scheme based on multireference perturbation theory calculations on a classical computer, using the QSCI wavefunction as a reference. This method was applied to ground and excited state calculations for two typical aromatic molecules, naphthalene and tetracene. The incorporation of the perturbation treatment was found to provide improved accuracy. Extension of the reference space based on the QSCI-selected configurations as a means of further improvement was also investigated.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hardness of classically sampling quantum chemistry circuits

    quant-ph 2025-04 accept novelty 7.0 of 10

    Single-layer unitary cluster Jastrow circuits can emulate quadratic-Ising IQP circuits, so sampling from some quantum chemistry ansaetze is worst-case classically hard unless the polynomial hierarchy collapses.

  2. Symmetry-adapted sample-based quantum diagonalization: Application to lattice model

    quant-ph 2025-05 conditional novelty 5.0 of 10

    A symmetry-adapted postprocessing step for sample-based quantum diagonalization improves energy convergence on the two-leg Hubbard ladder when combined with a momentum basis.

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