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Machine learning for sample-based quantum diagonalization: a review of generative configuration recovery and the classical-simulability frontier
Sample-based quantum diagonalization (SQD), equivalently quantum-selected configuration interaction (QSCI), has become a centre of gravity of pre-fault-tolerant quantum chemistry: a processor samples electronic configurations and the Hamiltonian is diagonalized classically in the resulting subspace.
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