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Quantum-centric computation of molecular excited states with extended sample-based quantum diagonalization

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arxiv 2411.00468 v1 pith:5M3HKUS6 submitted 2024-11-01 quant-ph cond-mat.otherphysics.chem-phphysics.comp-ph

classification quant-phcond-mat.otherphysics.chem-phphysics.comp-ph
keywords quantumdevicesexcitedstateselectronicmethodmolecularaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

The simulation of molecular electronic structure is an important application of quantum devices. Recently, it has been shown that quantum devices can be effectively combined with classical supercomputing centers in the context of the sample-based quantum diagonalization (SQD) algorithm. This allowed the largest electronic structure quantum simulation to date (77 qubits) and opened near-term devices to practical use cases in chemistry toward the hundred-qubit mark. However, the description of many important physical and chemical properties of those systems, such as photo-absorption/-emission, requires a treatment that goes beyond the ground state alone. In this work, we extend the SQD algorithm to determine low-lying molecular excited states. The extended-SQD method improves over the original SQD method in accuracy, at the cost of an additional computational step. It also improves over quantum subspace expansion based on single and double electronic excitations, a widespread approach to excited states on pre-fault-tolerant quantum devices, in both accuracy and efficiency. We employ the extended SQD method to compute the first singlet (S$_1$) and triplet (T$_1$) excited states of the nitrogen molecule with a correlation-consistent basis set, and the ground- and excited-state properties of the [2Fe-2S] cluster.

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Cited by 3 Pith papers

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

  1. Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier

    quant-ph 2026-08 conditional novelty 6.0 of 10

    A critical review plus small exact-FCI experiments concludes that sample-based quantum diagonalization has not beaten classical selected CI and maps where, if anywhere, a quantum or generative advantage could survive.

  2. Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization

    quant-ph 2026-03 conditional novelty 6.0 of 10

    A first-quantized CI-matrix QSCI variant reduces qubit count to O(log N) and gives accuracy on N2/naphthalene comparable to sample-based quantum diagonalization.

  3. Implicit solvent sample-based quantum diagonalization

    quant-ph 2025-02 conditional novelty 5.0 of 10

    SQD with IEF-PCM solvation reproduces CASCI IEF-PCM energies for four small molecules on IBM quantum hardware.

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