Probing quantum processor performance with pyGSTi

Abstract

PyGSTi is a Python software package for assessing and characterizing the performance of quantum computing processors. It can be used as a standalone application, or as a library, to perform a wide variety of quantum characterization, verification, and validation (QCVV) protocols on as-built quantum processors. We outline pyGSTi's structure, and what it can do, using multiple examples. We cover its main characterization protocols with end-to-end implementations. These include gate set tomography, randomized benchmarking on one or many qubits, and several specialized techniques. We also discuss and demonstrate how power users can customize pyGSTi and leverage its components to create specialized QCVV protocols and solve user-specific problems.

18 pages, 17 code listings, 2 figures

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Cited by in corpus (46)

• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Computing with spin qubits at the surface code error threshold
• Precision tomography of a three-qubit donor quantum processor in silicon
• Gate Set Tomography
• Measuring the Capabilities of Quantum Computers
• Universal logic with encoded spin qubits in silicon
• Modelling and Simulating the Noisy Behaviour of Near-term Quantum Computers
• Universal gate operations on nuclear spin qubits in an optical tweezer array of ${}^{171}$Yb atoms
• Machine learning of noise-resilient quantum circuits
• Benchmarking a trapped-ion quantum computer with 30 qubits
• Detecting and tracking drift in quantum information processors
• Non-Markovian Quantum Process Tomography
• Generation and verification of 27-qubit Greenberger-Horne-Zeilinger states in a superconducting quantum computer
• Operating semiconductor quantum processors with hopping spins
• Scalable randomized benchmarking of quantum computers using mirror circuits
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• Experimental Characterization of Crosstalk Errors with Simultaneous Gate Set Tomography
• Evaluating energy differences on a quantum computer with robust phase estimation
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• Estimating gate-set properties from random sequences
• Compressive gate set tomography
• Emulating two qubits with a four-level transmon qudit for variational quantum algorithms
• Demonstrating scalable randomized benchmarking of universal gate sets
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• Benchmarking quantum gates and circuits
• Benchmarking quantum logic operations relative to thresholds for fault tolerance
• Operational, gauge-free quantum tomography
• Fully scalable randomized benchmarking without motion reversal
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• Efficient flexible characterization of quantum processors with nested error models
• Pauli Noise Learning for Mid-Circuit Measurements
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• What can unitary sequences tell us about multi-time physics?
• Characterization of coherent errors in gate layers with robustness to Pauli noise
• Efficient Qubit Calibration by Binary-Search Hamiltonian Tracking
• A Theory of Direct Randomized Benchmarking
• Universal gates for a metastable qubit in strontium-88
• Streaming quantum gate set tomography using the extended Kalman filter
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