pith. the verified trust layer for science. sign in

arxiv: 1509.02921 · v1 · pith:XWLYWF3Ynew · submitted 2015-09-09 · 🪐 quant-ph

Introduction to Quantum Gate Set Tomography

classification 🪐 quant-ph
keywords quantumaccurategatetomographycharacterizationerrorsmethodregime
0
0 comments X p. Extension
Add this Pith Number to your LaTeX paper What is a Pith Number?
\usepackage{pith}
\pithnumber{XWLYWF3Y}

Prints a linked pith:XWLYWF3Y badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more

read the original abstract

Quantum gate set tomography (GST) has emerged as a promising method for the full characterization of quantum logic gates. In contrast to quantum process tomography (QPT), GST self-consistently and correctly accounts for state preparation and measurement (SPAM) errors. It therefore provides significantly more accurate estimates than QPT as gate fidelities increase into the fault-tolerant regime. We give a detailed review of GST and provide a self-contained guide to its implementation. The method is presented in a step-by-step fashion and relevant mathematical background material is included. Our goal is to demonstrate the utility of GST as both an accurate characterization technique and a simple and effective diagnostic tool. As an illustration, we compare the output of GST and QPT using simulated example data for a single qubit. In agreement with the original literature, we find that coherent errors are poorly estimated by QPT near quantum error correction thresholds, while GST is accurate in this regime.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data

    quant-ph 2026-05 unverdicted novelty 6.0

    A generative QMLC framework tokenizes GST data, embeds it via curriculum-trained set-vision transformers into a context-aware latent space, and uses diffusion models to synthesize circuits conditioned on desired measu...

  2. Learning to Concatenate Quantum Codes

    quant-ph 2026-04 unverdicted novelty 6.0

    A machine-learning approach adaptively chooses quantum code sequences for concatenation to achieve target logical error rates with far fewer qubits than standard methods for structured noise.

  3. Benchmarking Single-Qubit Gates on a Neutral Atom Quantum Processor

    quant-ph 2025-09 unverdicted novelty 5.0

    DRB and GST benchmarking on neutral-atom single-qubit gates yields 99.963% average fidelity, with consistent results on a 25-qubit array and a new gauge optimization for GST.

  4. Understanding Quantum Instruments

    quant-ph 2026-04 unverdicted novelty 2.0

    Quantum instrument errors are represented by outcome-specific d²×d² superoperators, but the joint quantum-classical nature requires careful interpretation beyond standard process matrices.