Prediction and real-time compensation of qubit decoherence via machine learning

Sandeep Mavadia, Virginia Frey, Jarrah Sastrawan, Stephen Dona and Michael J. Biercuk ()
Additional contact information
Sandeep Mavadia: ARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney
Virginia Frey: ARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney
Jarrah Sastrawan: ARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney
Stephen Dona: ARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney
Michael J. Biercuk: ARC Centre for Engineered Quantum Systems, School of Physics, The University of Sydney

Nature Communications, 2017, vol. 8, issue 1, 1-6

Abstract: Abstract The wide-ranging adoption of quantum technologies requires practical, high-performance advances in our ability to maintain quantum coherence while facing the challenge of state collapse under measurement. Here we use techniques from control theory and machine learning to predict the future evolution of a qubit’s state; we deploy this information to suppress stochastic, semiclassical decoherence, even when access to measurements is limited. First, we implement a time-division multiplexed approach, interleaving measurement periods with periods of unsupervised but stabilised operation during which qubits are available, for example, in quantum information experiments. Second, we employ predictive feedback during sequential but time delayed measurements to reduce the Dick effect as encountered in passive frequency standards. Both experiments demonstrate significant improvements in qubit-phase stability over ‘traditional’ measurement-based feedback approaches by exploiting time domain correlations in the noise processes. This technique requires no additional hardware and is applicable to all two-level quantum systems where projective measurements are possible.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/ncomms14106 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14106

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms14106

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().