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Quantum Machine Learning

Hands-on Introduction to Randomized Benchmarking

arXiv

Authors: Ana Silva, Eliska Greplova

Year

2024

Paper ID

38224

Status

Preprint

Abstract Read

~2 min

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Abstract

Randomized benchmarking techniques have been an essential tool for assessing the performance of contemporary quantum devices. The goal of this tutorial is to provide a pedagogical, self-contained, introduction to randomized benchmarking. With this intention, every chapter is also supplemented with an accompanying Python notebook, illustrating the essential steps of each protocol. In addition, we also introduce more recent trends in the field that bridge shadow tomography with randomized benchmarking, namely through the gate-set shadow protocol.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2024 reference point for readers tracking recent quantum research.
Randomized benchmarking techniques have been an essential tool for assessing the performance of contemporary quantum devices.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2410.08683 [2] arXiv https://arxiv.org/abs/2410.08683 [3] Publisher https://arxiv.org/abs/2410.08683

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