Diagnosing gate faults in quantum circuits using machine learning

Presenting Author: Margarite LaBorde, Louisiana State University
Contributing Author(s): Allee Rogers, Jonathan Dowling

We propose a procedure to diagnose where gate faults occur in a given circuit using a hybridize quantum-and-classical K-Nearest-Neighbors (KNN) machine learning technique. This is accomplished using a diagnostic circuit and selected input qubits to obtain the fidelity between output states of the altered circuit and a set of given reference states, providing a quantum analogy to the Euclidean distances used for KNN classification algorithms. The outcomes of the quantum circuit can then be stored to be used for a classical KNN algorithm. We demonstrate numerically an ability to locate a faulty gate in circuits with over 30 gates and up to 9 qubits with over 90% accuracy

(Session 5 : Saturday from 5:00pm - 7:00pm)


SQuInT Chief Organizer
Akimasa Miyake, Associate Professor

SQuInT Co-Organizer
Brian Smith, Associate Professor UO

SQuInT Program Committee
Postdoctoral Fellows:
Markus Allgaier (UO OMQ)
Sayonee Ray (UNM CQuIC)
Pablo Poggi (UNM CQuIC)
Valerian Thiel (UO OMQ)

SQuInT Event Co-Organizers (Oregon)
Jorjie Arden
Holly Lynn

SQuInT Event Administrator (Oregon)
Brandy Todd

SQuInT Administrator (CQuIC)
Gloria Cordova
505 277-1850

SQuInT Founder
Ivan Deutsch, Regents' Professor, CQuIC Director

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