Structure-Agnostic Unitary Learning from Quantum Observable Dynamics with Application to Hamiltonian Identification
Source: Originally published on arXiv quant-ph on July 20, 2026.
arXiv:2607.15316v1 Announce Type: new Abstract: We present a variational algorithm for learning an unknown quantum unitary from time-series observable measurements, with no structural assumption about the target. The core separation: a hardware-efficient parametrised circuit learns the evolution operator U via observable matching; Hamiltonian identification follows as classical post-processing via matrix logarithm, when the target happens to be exp(-iH*tau). Three experiments establish the method's generality. First, a noiseless proof of correctness with exact gradients (L-BFGS-B) achieves MSE 1.61e-14 and recovers all Hamiltonian coefficients to six decimal places. Second, a gate-learning experiment fits CNOT, iSWAP, and a Haar-random SU(4) element -- none generated by any fixed Hamiltoni...
To read the full article, visit the original source page: