Quantum Benchmark Zoo

A community-maintained catalog of protocols for measuring quantum computer performance — from single-gate characterization to full application suites. Each entry covers what the benchmark measures, how it works, key papers, and reference implementations.

All benchmarks

  • Estimates circuit fidelity by checking how often a device samples the high-probability bitstrings of random quantum circuits — the metric behind Google's quantum supremacy claim.

    Google Quantum AI · 2016 · also known as XEB, Linear XEB

  • Single-number, full-system benchmark that scores a device by the largest random square circuit it can run while still generating heavy outputs reliably.

    IBM · 2018 · also known as QV

  • Estimates average gate error from the exponential decay of survival probability over increasingly long random Clifford sequences, robust to state-preparation and measurement errors.

    Emerson, Alicki & Życzkowski, Knill et al. (NIST), Magesan, Gambetta & Emerson · 2005 · also known as RB, Clifford RB

  • Scalable, hardware-agnostic suite of eight application-level benchmarks, with a six-dimensional feature vector that profiles how each workload stresses a device.

    Super.tech (now Infleqtion), University of Chicago (EPiQC) · 2022

Browse by category

  • Component-level

    Characterize individual gates, qubits, and operations in isolation — error rates, coherence, and calibration quality.

  • System-level

    Exercise a whole processor with structured or random circuits to produce holistic scores that reflect qubit count, fidelity, connectivity, and the compiler together.

  • Application-level

    Measure end-to-end performance on programs representative of real workloads, from algorithm subroutines to full application suites.