Finding Broken Gates in Quantum Circuits---Exploiting Hybrid Machine Learning

• URL: http://arxiv.org/abs/2001.10939v1
• Date: Wed, 29 Jan 2020 16:25:54 GMT
• Title: Finding Broken Gates in Quantum Circuits---Exploiting Hybrid Machine Learning
• Authors: Margarite L. LaBorde, Allee C. Rogers, Jonathan P. Dowling
• Abstract summary: Current implementations of quantum logic gates can be highly faulty and introduce errors. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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