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20192026
most citedRobust shadow estimation

146 citations · 420 across the 26 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

quant-ph2024★ 5 cited

Efficient self-consistent learning of gate set Pauli noise

Senrui Chen, Zhihan Zhang, Liang Jiang +1

Understanding quantum noise is an essential step towards building practical quantum information processing systems. Pauli noise is a useful model that has been widely applied in qu…

quant-ph2024

Entanglement-enhanced learning of quantum processes at scale

Alireza Seif, Senrui Chen, Swarnadeep Majumder +6

Learning unknown processes affecting a quantum system reveals underlying physical mechanisms and enables suppression, mitigation, and correction of unwanted effects. Describing a g…

quant-ph2024★ 21 cited

A Practical Introduction to Benchmarking and Characterization of Quantum Computers

Akel Hashim, Long B. Nguyen, Noah Goss +16

Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthr…

quant-ph2024★ 9 cited

A generalized cycle benchmarking algorithm for characterizing mid-circuit measurements

Zhihan Zhang, Senrui Chen, Yunchao Liu +1

Mid-circuit measurements (MCMs) are crucial ingredients in the development of fault-tolerant quantum computation. While there have been rapid experimental progresses in realizing M…

quant-ph2024★ 17 cited

Entanglement-enabled advantage for learning a bosonic random displacement channel

Changhun Oh, Senrui Chen, Yat Wong +8

We show that quantum entanglement can provide an exponential advantage in learning properties of a bosonic continuous-variable (CV) system. The task we consider is estimating a pro…

quant-ph2024★ 7 cited

Universal Spreading of Conditional Mutual Information in Noisy Random Circuits

Su-un Lee, Changhun Oh, Yat Wong +2

We study the evolution of conditional mutual information in generic open quantum systems, focusing on one-dimensional random circuits with interspersed local noise. Unlike in noise…