Abstract: The Heisenberg limit is a fundamental limit imposed by the principles of quantum mechanics. It provides a lower bound of the amount of resources needed to achieve certain precision in an estimation task. Saturating the Heisenberg limit brings practical benefits to quantum algorithms and learning protocols. In this talk I will present two algorithms that saturate the Heisenberg limit in ground state energy estimation and in Hamiltonian learning respectively. No prior knowledge of quantum mechanics or quantum computing is assumed.

The seminar will also be broadcast via Zoom at the link https://umich.zoom.us/j/96021646996

Meeting ID: 960 2164 6996
Passcode: 034405

Contact: Shravan Veerapaneni