Current implementations of quantum logic gates can be highly faulty and introduce errors. In order to correct these errors, it is necessary to first identify the faulty gates. We demonstrate a procedure to diagnose where gate faults occur in a circuit by using a hybridized quantum-and-classical K-Nearest-Neighbors (KNN) machine-learning technique. We accomplish this task using a diagnostic circuit and selected input qubits to obtain the fidelity between a set of output states and reference states. The outcomes of the circuit can then be stored to be used for a classical KNN algorithm. We numerically demonstrate an ability to locate a faulty gate in circuits with over 30 gates and up to nine qubits with over 90% accuracy.
@article{arxiv.2001.10939,
title = {Finding Broken Gates in Quantum Circuits---Exploiting Hybrid Machine Learning},
author = {Margarite L. LaBorde and Allee C. Rogers and Jonathan P. Dowling},
journal= {arXiv preprint arXiv:2001.10939},
year = {2021}
}