Featuremetric benchmarking: Quantum computer benchmarks based on circuit features
This provides a more detailed benchmarking method for quantum computing researchers, though it is incremental as it builds on volumetric benchmarking.
The authors tackled the problem of benchmarking quantum computers by introducing a framework that models performance as a function of circuit features like depth and width, generalizing existing volumetric methods. They demonstrated this on IBM Q and IonQ systems with up to 27 qubits, showing it enables richer performance models.
Benchmarks that concisely summarize the performance of many-qubit quantum computers are essential for measuring progress towards the goal of useful quantum computation. In this work, we present a benchmarking framework that is based on quantifying how a quantum computer's performance on quantum circuits varies as a function of features of those circuits, such as circuit depth, width, two-qubit gate density, problem input size, or algorithmic depth. Our featuremetric benchmarking framework generalizes volumetric benchmarking -- a widely-used methodology that quantifies performance versus circuit width and depth -- and we show that it enables richer and more faithful models of quantum computer performance. We demonstrate featuremetric benchmarking with example benchmarks run on IBM Q and IonQ systems of up to 27 qubits, and we show how to produce performance summaries from the data using Gaussian process regression. Our data analysis methods are also of interest in the special case of volumetric benchmarking, as they enable the creation of intuitive two-dimensional capability regions using data from few circuits.