PIRSA:23110056

Neural-Shadow Quantum State Tomography

Wei, V. (2023). Neural-Shadow Quantum State Tomography. Perimeter Institute. https://pirsa.org/23110056

Wei, Victor. Neural-Shadow Quantum State Tomography. Perimeter Institute, Nov. 10, 2023, https://pirsa.org/23110056 

          @misc{ pirsa_PIRSA:23110056,
            doi = {10.48660/23110056},
            url = {https://pirsa.org/23110056},
            author = {Wei, Victor},
            keywords = {Other},
            language = {en},
            title = {Neural-Shadow Quantum State Tomography},
            publisher = {Perimeter Institute},
            year = {2023},
            month = {nov},
            note = {PIRSA:23110056 see, \url{https://pirsa.org}}
          }

Victor Wei

University of Waterloo

Talk number
PIRSA:23110056
Collection
Talk Type
Subject
Abstract

Quantum state tomography (QST) is the art of reconstructing an unknown quantum state through measurements. It is a key primitive for developing quantum technologies. Neural network quantum state tomography (NNQST), which aims to reconstruct the quantum state via a neural network ansatz, is often implemented via a basis-dependent cross-entropy loss function. State-of-the-art implementations of NNQST are often restricted to characterizing a particular subclass of states, to avoid an exponential growth in the number of required measurement settings. In this talk, I will discuss an alternative neural-network-based QST protocol that uses shadow-estimated infidelity as the loss function, named “neural-shadow quantum state tomography” (NSQST). After introducing NNQST and the classical shadow formalism, I will present numerical results on the advantage of NSQST over NNQST at learning the relative phases, NSQST’s noise robustness, and NSQST’s advantage over direct shadow estimation. I will also briefly discuss the future prospects of the protocol with different variational ansatz and randomized measurements, as well as its experimental feasibility.

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Zoom link https://pitp.zoom.us/j/94167105773?pwd=TXR3TUtwNjV4VFB4SEpvTkhqd29SUT09