Characterizing Qubits: Randomized Benchmarking and Tomography

Characterizing Qubits: Randomized Benchmarking and Tomography

Before trusting a quantum device, you need to measure how well its qubits and gates actually perform, and this Cirq tutorial demonstrates several standard characterization techniques for doing exactly that. It covers Clifford-based randomized benchmarking, the workhorse method for estimating average gate error rates in a way that is robust to state-preparation and measurement errors, by running random sequences of Clifford gates of increasing length and observing how the survival probability decays with sequence depth. The example shows how to sweep the number of Cliffords, for instance testing depths of 10, 15, 20, and 25, and fit the resulting decay curve to extract a per-gate error. It also demonstrates state tomography, which reconstructs the full density matrix of a prepared quantum state from a complete set of measurements, letting you visualize exactly what state the device produced. Together these methods form the practical toolkit for benchmarking and debugging quantum hardware. The script walks through configuring and running each experiment and plotting the results, making it a hands-on, intermediate-level guide to the characterization techniques that quantum hardware development relies on day to day.

Run it

pip install -r requirements.txt
python qubit_characterizations_example.py

Source and license

Imported from examples/qubit_characterizations_example.py in quantumlib/Cirq at v1.6.1, under the Apache License 2.0. Original authors: The Cirq Developers. The upstream LICENSE is included alongside this example.