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A short note on effective Pauli noise models

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Michael A. Perlin

Quantum Physics · Submitted 15 Nov 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This prescription yields the closest Pauli channel approximation to the error channel associated with the gate implementation, as measured by the Frobenius distance between quantum channels.

how it is stated · asserted 1.00 read the record →

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arXiv ID2311.09129 (version 1)
Pith IDCFRDG7XT, Pith's permanent record of this paper and its review
CitationMichael A. Perlin. "A short note on effective Pauli noise models." arXiv:2311.09129 (2023). https://pith.science/paper/CFRDG7XT
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,883 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.27 · human participants 0.02 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.22 · compares with prior results 0.16 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We provide a simple prescription to extract an effective Pauli noise model from classical simulations of a noisy experimental protocol for a unitary gate. This prescription yields the closest Pauli channel approximation to the error channel associated with the gate implementation, as measured by the Frobenius distance between quantum channels. Informed by these results, we highlight some puzzles regarding the quantitative treatment of coherent errors.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We provide a simple prescription to extract an effective Pauli noise model from classical simulations of a noisy experimental protocol for a unitary gate. contribution 0.87 0.94 0.34 asserted 1.00
s1 This prescription yields the closest Pauli channel approximation to the error channel associated with the gate implementation, as measured by the Frobenius distance between quantum channels. result 0.97 0.83 0.50 asserted 1.00
s2 Informed by these results, we highlight some puzzles regarding the quantitative treatment of coherent errors. interpretation 0.98 0.58 0.10 asserted 0.79

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from proof or derivation 0.58.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We provide a simple prescription to extract an effective Pauli noise model from classical simulations of a noisy experimental protocol for a unitary gate. This prescription yields the closest Pauli channel approximation to the error channel associated with the gate implementation, as measured by the Frobenius distance between quantum channels. Informed by these results, we highlight some puzzles regarding the quantitative treatment of coherent errors.

Human review

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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