Active Sampling Sample-based Quantum Diagonalization from Finite-Shot Measurements
Near-term quantum devices provide only finite-shot measurements and prepare imperfect, contaminated states. This motivates algorithms that convert samples into reliable low-energy estimates without full tomography or exhaustive measurements. We propose Active Sampling Sample-based Quantum Diagonalization (AS-SQD), framing SQD as an active learning problem: given measured bitstrings, which additional basis states should be included to efficiently recover the ground-state energy? SQD restricts the Hamiltonian to a selected set of basis states and classically diagonalizes the restricted matrix. However, naive SQD using only sampled states suffers from bias under finite-shot sampling and exci...
| Authors | Rinka Miura |
|---|---|
| Primary category | quant-ph |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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