AS-SQD applies an active sampling strategy with Epstein-Nesbet perturbation scores to iteratively expand the basis in sample-based quantum diagonalization, achieving lower energy errors than standard or random methods on spin chains and IBM hardware.
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Reinforcement reduces quantum search time from √D to ln D and exponentially improves noise tolerance via numerical simulations on qubits and qudits.
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Active Sampling Sample-based Quantum Diagonalization from Finite-Shot Measurements
AS-SQD applies an active sampling strategy with Epstein-Nesbet perturbation scores to iteratively expand the basis in sample-based quantum diagonalization, achieving lower energy errors than standard or random methods on spin chains and IBM hardware.
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Noise tolerance via reinforcement in the quantum search problem
Reinforcement reduces quantum search time from √D to ln D and exponentially improves noise tolerance via numerical simulations on qubits and qudits.