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Quantum Information Processing

Finding broken gates in quantum circuits: exploiting hybrid machine learning

  • 01-08-2020
Published in:
Authors
Margarite L. LaBorde
Allee C. Rogers
Jonathan P. Dowling
Published in
Quantum Information Processing | Issue 8/2020

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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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previous article Quantum strategies for simple two-player XOR games
next article A unitary operator construction solution based on Pauli group for maximal dense coding with a class of symmetric states
Title
Finding broken gates in quantum circuits: exploiting hybrid machine learning
Authors
Margarite L. LaBorde
Allee C. Rogers
Jonathan P. Dowling
Publication date
01-08-2020
Publisher
Springer US
Published in
Quantum Information Processing / Issue 8/2020
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-020-02729-y
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