Predicting quantum channels over general product distributions
Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane, Lange, Jerry Li

TL;DR
This paper introduces a new quantum channel prediction method that works effectively over general product distributions, overcoming previous limitations to specific invariant distributions, and employs a biased Pauli analysis technique.
Contribution
The authors develop a novel approach for predicting quantum channels over arbitrary product distributions, expanding beyond prior work limited to Clifford-invariant distributions.
Findings
Achieves accurate quantum channel prediction over most product distributions.
Introduces a biased Pauli analysis technique for quantum information.
Addresses challenges due to lack of orthogonal basis in quantum setting.
Abstract
We investigate the problem of predicting the output behavior of unknown quantum channels. Given query access to an -qubit channel and an observable , we aim to learn the mapping \begin{equation*} \rho \mapsto \mathrm{Tr}(O E[\rho]) \end{equation*} to within a small error for most sampled from a distribution . Previously, Huang, Chen, and Preskill proved a surprising result that even if is arbitrary, this task can be solved in time roughly , where is the target prediction error. However, their guarantee applied only to input distributions invariant under all single-qubit Clifford gates, and their algorithm fails for important cases such as general product distributions over product states . In this work, we propose a new approach that achieves accurate prediction over essentially any product distribution ,…
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Taxonomy
TopicsAdvancements in Semiconductor Devices and Circuit Design · Quantum Computing Algorithms and Architecture · Machine Learning in Materials Science
