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This paper presents SqDMRG, a hybrid algorithm merging sample-based quantum diagonalization with density matrix renormalization group techniques to solve Hamiltonians for molecular clusters like benzene pentamers. By leveraging samples from the IBM Quantum Nighthawk r2 processor alongside classical references, the method achieves sub-kcal/mol accuracy while significantly reducing the number of required configurations compared to direct diagonalization. The work advances practical quantum simulation capabilities by efficiently expanding sampled determinant spaces into fragment product spaces for complex chemical systems.
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Sample-based Quantum Diagonalization via Density Matrix Renormalization Group

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arXiv:2611.05975v1 Announce Type: new Abstract: We introduce SqDMRG, which combines sample-based quantum diagonalization (SQD) with the density matrix renormalization group (DMRG). SqDMRG expands the sampled determinant space into a product of fragment spaces constructed from the samples, and solves the Hamiltonian in this product space using DMRG. We demonstrate the method on benzene pentamer and decamer clusters using classical reference samples, and on the pentamer using quantum samples obtained from the IBM Quantum Nighthawk r2 processor ibm phoenix. It achieves sub-kcal/mol accuracy with far fewer configurations than direct diagonalization.

Abstract:We introduce SqDMRG, which combines sample-based quantum diagonalization (SQD) with the density matrix renormalization group (DMRG). SqDMRG expands the sampled determinant space into a product of fragment spaces constructed from the samples, and solves the Hamiltonian in this product space using DMRG. We demonstrate the method on benzene pentamer and decamer clusters using classical reference samples, and on the pentamer using quantum samples obtained from the IBM Quantum Nighthawk r2 processor ibm phoenix. It achieves sub-kcal/mol accuracy with far fewer configurations than direct diagonalization.

arXiv:2611.05975 [quant-ph]

(or arXiv:2611.05975v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2611.05975 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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