Neural Network Based Qubit Environment Characterization

The exact microscopic structure of the environments that produces \(1/f\) noise in superconducting qubits remains largely unknown, hindering our ability to have robust simulations and harness the noise. In this paper we show how it is possible to infer information about such an environment based on...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Papič, Miha, de Vega, Inés
Format: Article
Language:English
Subjects:
Algorithms
Environment models
Impurities
Machine learning
Neural networks
Noise
Qubits (quantum computing)
Structural analysis
Superconductivity
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The exact microscopic structure of the environments that produces \(1/f\) noise in superconducting qubits remains largely unknown, hindering our ability to have robust simulations and harness the noise. In this paper we show how it is possible to infer information about such an environment based on a single measurement of the qubit coherence, circumventing any need for separate spectroscopy experiments. Similarly to other spectroscopic techniques, the qubit is used as a probe which interacts with its environment. The complexity of the relationship between the observed qubit dynamics and the impurities in the environment makes this problem ideal for machine learning methods - more specifically neural networks. With our algorithm we are able to reconstruct the parameters of the most prominent impurities in the environment, as well as differentiate between different environment models, paving the way towards a better understanding of \(1/f\) noise in superconducting circuits.
ISSN:2331-8422
DOI:10.48550/arxiv.2110.05465