Machine learning of noise in single-qubit hardware

Presenting Author: Travis Scholten, University of New Mexico CQuIC

Techniques for characterizing quantum information processing hardware — e.g., as-built qubits — are generally based on ad-hoc or statistical methods for analyzing data.  Machine learning provides a different paradigm for qubit characterization methods.  It promises greater efficiency, the ability to handle large datasets, and automated tool-building.  I will present progress toward these desiderata via two distinct but related projects.  First, I demonstrate a support vector machine classifier that learns how to distinguish whether the noise afflicting a single-qubit QIP is predominantly stochastic or predominantly coherent.  It analyzes data from the structured circuits used for gate set tomography (GST), but avoids all the standard statistical tools used for GST analysis.  Second, I demonstrate how to automate the selection of a sparse subset of those circuits that is maximally useful for classifying such noise.​ Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

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


SQuInT Chief Organizer
Akimasa Miyake, Assistant Professor

SQuInT Co-Organizer
Mark M. Wilde, Assistant Professor LSU

SQuInT Administrator
Gloria Cordova
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SQuInT Founder
Ivan Deutsch, Regents' Professor

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