Cross-Entropy Benchmarking (XEB)
Cross-entropy benchmarking (XEB) is a statistical method for evaluating quantum processor performance by measuring how well experimental output distributions of random circuits correlate with ideal distributions. Google used linear XEB to demonstrate quantum computational advantage on the 53-qubit Sycamore processor in 2019. The XEB fidelity decays exponentially with circuit depth, providing a measure of cumulative gate quality.[1]
- Algorithm: Linear cross-entropy on random quantum circuits
- Category: other
- Qubits: 53
- Framework: Cirq
- Hardware: Google Sycamore (superconducting)
- Reproducible: Yes
- Published:
- XEB
- quantum-supremacy
- Sycamore
- random-circuits
What algorithm does Cross-Entropy Benchmarking (XEB) use?
Cross-Entropy Benchmarking (XEB) uses the Linear cross-entropy on random quantum circuits algorithm, categorized under other.
Frequently Asked Questions
What is the Cross-Entropy Benchmarking (XEB) benchmark?
Cross-entropy benchmarking (XEB) is a statistical method for evaluating quantum processor performance by measuring how well experimental output distributions of random circuits correlate with ideal distributions. Google used linear XEB to demonstrate quantum computational advantage on the 53-qubit Sycamore processor in 2019. The XEB fidelity decays exponentially with circuit depth, providing a measure of cumulative gate quality.
Is Cross-Entropy Benchmarking (XEB) reproducible?
Yes, this benchmark is reproducible.
Sources
- "Cross-Entropy Benchmarking Theory - Google Cirq Documentation", accessed 2026-03-19 — quantumai.google
- "On the Classical Hardness of Spoofing Linear Cross-Entropy Benchmarking", accessed 2026-03-19 — arXiv