## Video URL

http://pirsa.org/23110056# Neural-Shadow Quantum State Tomography

### APA

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

### MLA

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

### BibTex

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

Victor Wei University of Waterloo

## 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