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Predictive Models from Quantum Computer Benchmarks
In the first case study, we predict circuit (i) process fidelities and (ii) success probabilities by fitting error rates models to two kinds of volumetric benchmarking data.
Learning a quantum computer's capability using convolutional neural networks
We explore how to define the capability function, and we explain how data for training neural networks can be efficiently obtained for a capability function defined using process fidelity.
Characterizing mid-circuit measurements on a superconducting qubit using gate set tomography
Measurements that occur within the internal layers of a quantum circuit -- mid-circuit measurements -- are an important quantum computing primitive, most notably for quantum error correction.
Quantum Physics
A taxonomy of small Markovian errors
Errors in quantum logic gates are usually modeled by quantum process matrices (CPTP maps).
Quantum Physics
Wildcard error: Quantifying unmodeled errors in quantum processors
Using both simulated and experimental data, we show how to use wildcard error to reconcile error models derived from RB and GST experiments with inconsistent data, to capture non-Markovianity, and to quantify all of a processor's observed error.
Quantum Physics
Detecting and tracking drift in quantum information processors
If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed.
Quantum Physics Atomic Physics Data Analysis, Statistics and Probability
