Featuremetric benchmarking: Quantum computer benchmarks based on circuit features

2025-04-17

AI Summary

Existing quantum hardware benchmarks lack fine-grained, interpretable performance evaluation for multi-qubit processors. Method: This paper proposes a feature-driven quantum hardware benchmarking framework that replaces volumetric benchmarks with structural circuit features—namely, circuit depth, width, and two-qubit gate density—as core modeling dimensions. Integrating circuit feature engineering with Gaussian process regression, the framework constructs interpretable 2D capability maps from sparse benchmarking data. Results: Evaluated on real IBM Q (up to 27 qubits) and IonQ devices, the framework significantly improves fidelity and generalizability of performance modeling while reducing the required number of benchmark circuits by an order of magnitude. Its primary contribution is establishing a quantitative, interpretable mapping paradigm between circuit features and hardware performance—providing a scalable, explainable assessment infrastructure to guide quantum hardware co-design and iteration.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Develop benchmarks for quantum computer performance evaluation
• Generalize volumetric benchmarking with richer performance models
• Analyze performance using Gaussian process regression methods

Innovation

• Featuremetric benchmarking generalizes volumetric benchmarking
• Uses Gaussian process regression for performance summaries
• Analyzes performance via circuit features like depth