Software for Creating Scalable Benchmarks from Quantum Algorithms

TL;DR

This paper introduces 'scarab', a software tool that enables the creation of scalable, reliable quantum benchmarks from user-defined circuits, using efficient fidelity estimation methods suitable for large quantum systems.

Contribution

The work presents 'scarab', a novel software platform that simplifies the creation of scalable quantum benchmarks with well-motivated metrics, even for circuits with millions of qubits.

Findings

Enables creation of efficient benchmarks from large quantum circuits.

Demonstrates flexibility in benchmarking various quantum hardware and algorithms.

Uses advanced fidelity estimation techniques for scalability.

Abstract

Creating scalable, reliable, and well-motivated benchmarks for quantum computers is challenging: straightforward approaches to benchmarking suffer from exponential scaling, are insensitive to important errors, or use poorly-motivated performance metrics. Furthermore, curated benchmarking suites cannot include every interesting quantum circuit or algorithm, which necessitates a tool that enables the easy creation of new benchmarks. In this work, we introduce a software tool for creating scalable and reliable benchmarks that measure a well-motivated performance metric (process fidelity) from user-chosen quantum circuits and algorithms. Our software, called $\texttt{scarab}$, enables the creation of efficient and robust benchmarks even from circuits containing thousands or millions of qubits, by employing efficient fidelity estimation techniques, including mirror circuit fidelity estimation and subcircuit volumetric benchmarking. $\texttt{scarab}$ provides a simple interface that enables the creation of reliable benchmarks by users who are not experts in the theory of quantum computer benchmarking or noise. We demonstrate the flexibility and power of $\texttt{scarab}$ by using it to turn existing inefficient benchmarks into efficient benchmarks, to create benchmarks that interrogate hardware and algorithmic trade-offs in Hamiltonian simulation, to quantify the in-situ efficacy of approximate circuit compilation, and to create benchmarks that use subcircuits to measure progress towards executing a circuit of interest.