Abstract:
Estimating properties of unknown unitary operations is a fundamental task in quantum information science. While full unitary tomography requires a number of samples to the unknown unitary scaling linearly with the dimension (implying exponentially with the number of qubits), estimating specific functions of a unitary can be significantly more efficient. In this paper, we present a unified framework for the sample-efficient estimation of arbitrary square integrable functions f : U(d) → C, using only access to the controlled-unitary operation. We first provide a tight characterization of the optimal sample complexity when the accuracy is measured by the averaged bias over the unitary U(d). We then construct a sample-efficient estimation algorithm that becomes optimal under the Probably Approximately Correct (PAC) learning criterion for various classes of functions.
Based on: https://scirate.com/arxiv/2509.05710
I will then explore some other topics related to my earlier works, provided time permits.
Time and Place: Oct 8, 2025, 02:00 PM Warsaw, room 203
Remote guests are invited via Zoom:
