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Quantum Characterization Verification & Validation (QCVV) Tutorial: Calibration-fee Characterization (invited).

Kenneth Rudinger

Published Mar 13, 2017• 0 citations
OpenAlex

Abstract

SAND2016-1803C  We need "calibration-free" ways of extracting tomographic information. In this episode, we'll review & contrast several modern methods:  Randomized Benchmarking Tomography  Robust Phase Estimation  Gate Set Tomography 2 • (worst-case) error metrics • more than process tomography… • more than benchmarking… Fault Tolerance!Calibration-free characterization!

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SAND2016-1901PE Randomized benchmarking is calibration-free! Random-ish sequences  Average decay rates (infidelities). Relatively insensitive to unitary errors. Variants (e.g., RBT, URB) can extract more information, but not entire process matrices and not at Heisenberg limit. RB a useful tool, but incomplete.8

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Practical Introduction to Benchmarking and Characterization of Quantum Computers

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

Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthroughs in quantum algorithms, quantum simulations, and quantum error correction are bringing useful quantum computation closer to fruition. These remarkable achievements have been facilitated by advances in quantum characterization, verification, and validation (QCVV). QCVV methods and protocols enable scientists and engineers to scrutinize, understand, and enhance the performance of quantum information-processing devices. In this tutorial, we review the fundamental principles underpinning QCVV, and introduce a diverse array of QCVV tools used by quantum researchers. We define and explain QCVV’s core models and concepts—quantum states, measurements, and processes—and illustrate how these building blocks are leveraged to examine a target system or operation. We survey and introduce protocols ranging from simple qubit characterization to advanced benchmarking methods. Along the way, we provide illustrated examples and detailed descriptions of the protocols, highlight the advantages and disadvantages of each, and discuss their potential scalability to future large-scale quantum computers. This tutorial serves as a guidebook for researchers unfamiliar with the benchmarking and characterization of quantum computers, and also as a detailed reference for experienced practitioners.

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Megan Dahlhauser, Kevin C Young, Timothy J Proctor +1 more

Highlights• Generalized for many common quantum characterization, verification, and validation models • Identify if a model is consistent with data, and if not, where the inconsistencies are • Explore trends of inconsistencies with various features, such as circuit depth or gate types • Report quantifiable differences among experiment, model, and target SummaryWe are developing methods to determine and report characterization performance on circuit prediction tasks using statistical tests and simulation.We are demonstrating this process in experiment using gate set tomography to evaluate performance on circuit prediction tasks.We aim to create a validation process that is an effective tool in identifying characterization shortcomings and upgrading methodologies to create more accurate characterizations of quantum devices.

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Quantum Characterization, Verification, and Validation

Robin Blume-Kohout, Timothy J Proctor, Kevin C Young

Quantum characterization, verification, and validation (QCVV) is a set of techniques to probe, describe, and assess the behavior of quantum bits (qubits), quantum information-processing registers, and quantum computers. QCVV protocols probe and describe the effects of unwanted decoherence so that it can be eliminated or mitigated. They can be usefully divided into characterization techniques that estimate predictive models for a device's behavior from data, and benchmarking techniques that assess overall performance of a device. In this introductory article, we briefly summarize the history of QCVV, introduce the mathematical models and metrics upon which it relies, and then summarize the foundational fields of tomography, randomized benchmarking, and holistic benchmarks. We conclude with brief descriptions of (and references to) advanced topics including gate set tomography, phase estimation, Pauli noise learning, characterization of mid-circuit measurements and non-Markovianity, classical shadows, verification and certification, and logical qubit assessment.

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Python GST Implementation (PyGSTi) v. 0.9

Nielsen, Erik, Robin Blume-Kohout, Kenneth Rudinger +11 more

PyGSTi is a Python package for modeling and characterizing noise (errors) in small quantum information processors. In addition to being a basic framework for describing quantum circuits and noise models, it implements mainstream quantum characterization, verification, and validation (QCVV) protocols such as Gate Set Tomography (GST), Randomized Benchmarking (RB), Robust Phase Estimation, and Idle Tomography. It also implements prototype protocols used for timeseries analysis and crosstalk detection, all of which have the goal of better understanding the noise found in existing as-built experimental devices. The central protocol of pyGSTi (from where it derives its name) is Gate Set Tomography. GST is a theory and protocol for simultaneously estimating the state preparation, gate operations, and measurement effects of a physical system of one or many quantum bits (qubits). These estimates are based entirely on the statistics of experimental measurements, and their interpretation and analysis can provide a detailed understanding of the types of errors/imperfections in the physical system. In this way, GST provides not only a means of certifying the “goodness” of qubits but also a means of debugging (i.e. improving) them. The other protocols follow this similar pattern in that they use statistical inference and analysis of experimental data to estimate one or more properties of the noise in a device.

•Jan 1, 2019•1 citation
SAND2016-1803C  We need "calibration-free" ways of extracting tomographic information. In this episode, we'll review & contrast several modern methods:  Randomized Benchmarking Tomography  Robust Phase Estimation  Gate Set Tomography 2 • (worst-case) error metrics • more than process tomography… • more than benchmarking… Fault Tolerance!Calibration-free characterization!
Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthroughs in quantum algorithms, quantum simulations, and quantum error correction are bringing useful quantum computation closer to fruition. These remarkable achievements have been facilitated by advances in quantum characterization, verification, and validation (QCVV). QCVV methods and protocols enable scientists and engineers to scrutinize, understand, and enhance the performance of quantum information-processing devices. In this tutorial, we review the fundamental principles underpinning QCVV, and introduce a diverse array of QCVV tools used by quantum researchers. We define and explain QCVV’s core models and concepts—quantum states, measurements, and processes—and illustrate how these building blocks are leveraged to examine a target system or operation. We survey and introduce protocols ranging from simple qubit characterization to advanced benchmarking methods. Along the way, we provide illustrated examples and detailed descriptions of the protocols, highlight the advantages and disadvantages of each, and discuss their potential scalability to future large-scale quantum computers. This tutorial serves as a guidebook for researchers unfamiliar with the benchmarking and characterization of quantum computers, and also as a detailed reference for experienced practitioners.
Highlights• Generalized for many common quantum characterization, verification, and validation models • Identify if a model is consistent with data, and if not, where the inconsistencies are • Explore trends of inconsistencies with various features, such as circuit depth or gate types • Report quantifiable differences among experiment, model, and target SummaryWe are developing methods to determine and report characterization performance on circuit prediction tasks using statistical tests and simulation.We are demonstrating this process in experiment using gate set tomography to evaluate performance on circuit prediction tasks.We aim to create a validation process that is an effective tool in identifying characterization shortcomings and upgrading methodologies to create more accurate characterizations of quantum devices.
Quantum characterization, verification, and validation (QCVV) is a set of techniques to probe, describe, and assess the behavior of quantum bits (qubits), quantum information-processing registers, and quantum computers. QCVV protocols probe and describe the effects of unwanted decoherence so that it can be eliminated or mitigated. They can be usefully divided into characterization techniques that estimate predictive models for a device's behavior from data, and benchmarking techniques that assess overall performance of a device. In this introductory article, we briefly summarize the history of QCVV, introduce the mathematical models and metrics upon which it relies, and then summarize the foundational fields of tomography, randomized benchmarking, and holistic benchmarks. We conclude with brief descriptions of (and references to) advanced topics including gate set tomography, phase estimation, Pauli noise learning, characterization of mid-circuit measurements and non-Markovianity, classical shadows, verification and certification, and logical qubit assessment.
PyGSTi is a Python package for modeling and characterizing noise (errors) in small quantum information processors. In addition to being a basic framework for describing quantum circuits and noise models, it implements mainstream quantum characterization, verification, and validation (QCVV) protocols such as Gate Set Tomography (GST), Randomized Benchmarking (RB), Robust Phase Estimation, and Idle Tomography. It also implements prototype protocols used for timeseries analysis and crosstalk detection, all of which have the goal of better understanding the noise found in existing as-built experimental devices. The central protocol of pyGSTi (from where it derives its name) is Gate Set Tomography. GST is a theory and protocol for simultaneously estimating the state preparation, gate operations, and measurement effects of a physical system of one or many quantum bits (qubits). These estimates are based entirely on the statistics of experimental measurements, and their interpretation and analysis can provide a detailed understanding of the types of errors/imperfections in the physical system. In this way, GST provides not only a means of certifying the “goodness” of qubits but also a means of debugging (i.e. improving) them. The other protocols follow this similar pattern in that they use statistical inference and analysis of experimental data to estimate one or more properties of the noise in a device.