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Symmetry-adapted sample-based quantum diagonalization: Application to lattice model
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We present a symmetry-adapted extension of sample-based quantum diagonalization (SQD) that rigorously embeds space-group symmetry into the many-body subspace sampled by quantum hardware. The method is benchmarked on the two-leg ladder Hubbard model using both molecular orbital and momentum bases. Energy convergence is shown to be improved in the momentum basis compared to the molecular orbital basis for both the spin-quintet ground state and the spin-singlet excited state. We clarify the relationship between the compactness of the many-body wave function and the sparsity of the representation matrices of symmetry operations. Furthermore, the enhancement of the superconducting correlation function due to the Coulomb interaction is demonstrated. Our method highlights the importance of symmetry structure in random-sampling quantum simulation of correlated systems
Forward citations
Cited by 2 Pith papers
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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier
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.
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Quantum-Centric Geometry Optimization with Wave-Function-Based Embedding
EWF-SQD geometry optimization on real IBM quantum hardware reproduces classical SCI benchmark geometries within 4 pm for organic molecules up to 29 atoms (STO-3G).
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