Optimal Ansatz-free Hamiltonian Learning In Situ
Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction. Recent works have proposed protocols achieving the optimal Heisenberg-limited scaling learning ansatz-free Hamiltonians from their real-time evolutions without fully specifying interaction structures. However, these protocols rely on both deep circuits with interleaving probes and control, and extremely short time resolution, making them difficult to implement on near- and intermediate-term in situ quantum experiments. In this work, we propose a computationally efficient, control-free, and ancilla-free algorithm ...
| Authors | Taiqi Zhou, Weiyuan Gong |
|---|---|
| Primary category | quant-ph |
| Published | 17 June 2026 |
| Last updated at source | 28 July 2026 |
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