Learning the eigenstructure of quantum dynamics using classical shadows

TL;DR

This paper introduces a method for learning quantum dynamics by estimating the eigenstructure of the Choi matrix using classical shadows, enabling efficient quantum process tomography under certain constraints.

Contribution

It proposes a novel eigenstructure-based approach for quantum channel estimation using classical shadows and noise analysis, improving process tomography.

Findings

Reconstruction accuracy improves with more samples.

Eigenvalues of the Choi matrix reveal structure for certain quantum channels.

Noise effects on eigenspectrum are characterized using random matrix theory.

Abstract

Learning dynamics from repeated observation of the time evolution of an open quantum system, namely, the problem of quantum process tomography is an important task. This task is difficult in general, but, with some additional constraints could be tractable. This motivates us to look at the problem of Lindblad operator discovery from observations. We point out that for moderate size Hilbert spaces, low Kraus rank of the channel, and short time steps, the eigenvalues of the Choi matrix corresponding to the channel have a special structure. We use the least-square method for the estimation of a channel where, for fixed inputs, we estimate the outputs by classical shadows. The resultant noisy estimate of the channel can then be denoised by diagonalizing the nominal Choi matrix, truncating some eigenvalues, and altering it to a genuine Choi matrix. This processed Choi matrix is then compared to the original one. We see that as the number of samples increases, our reconstruction becomes more accurate. We also use tools from random matrix theory to understand the effect of estimation noise in the eigenspectrum of the estimated Choi matrix.