PIRSA:26040095

Video URL

https://pirsa.org/26040095

Instance-optimal high-precision shadow tomography with few-copy measurements

Zhou, S. (2026). Instance-optimal high-precision shadow tomography with few-copy measurements. Perimeter Institute for Theoretical Physics. https://pirsa.org/26040095

Zhou, Sisi. Instance-optimal high-precision shadow tomography with few-copy measurements. Perimeter Institute for Theoretical Physics, Apr. 15, 2026, https://pirsa.org/26040095 

          @misc{ scivideos_PIRSA:26040095,
            doi = {10.48660/26040095},
            url = {https://pirsa.org/26040095},
            author = {Zhou, Sisi},
            keywords = {Quantum Information},
            language = {en},
            title = {Instance-optimal high-precision shadow tomography with few-copy measurements},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2026},
            month = {apr},
            note = {PIRSA:26040095 see, \url{https://scivideos.org/pirsa/26040095}}
          }

Sisi Zhou

Talk numberPIRSA:26040095
DOI10.48660/26040095
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

We give the first instance-optimal sample complexity bounds for shadow tomography using few-copy measurements in the high-precision regime. More concretely, we study the problem of learning expectation values of a given set of observables of an unknown quantum state to precision $\epsilon$ in $L_p$-norm, using (possibly adaptive) measurements that act on one or a few copies at a time, and we are interested in the regime that $\epsilon$ is below some concrete and potentially dimension-dependent threshold. In this setup, we prove the necessary and sufficient number of copies, for any given set of observables, is characterized by a simple optimization formula involving a quadratic form of the inverse Fisher information matrix up to a logarithmic factor. Our results establish a rigorous correspondence between quantum learning and quantum metrology.