Real-time adaptive estimation of decoherence timescales for a single qubit

Arshad, Muhammad Junaid; Bekker, Christiaan; Haylock, Ben; Skrzypczak, Krzysztof; White, Daniel; Griffiths, Benjamin; Gore, Joe; Morley, Gavin W.; Salter, Patrick; Smith, Jason; Zohar, Inbar; Finkler, Amit; Altmann, Yoann; Gauger, Erik M.; Bonato, Cristian

doi:10.1103/physrevapplied.21.024026

Phys. Rev. Applied

2024

DOI: 10.1103/physrevapplied.21.024026

|View full text |Cite

Real-time adaptive estimation of decoherence timescales for a single qubit

Muhammad Junaid Arshad,

Christiaan Bekker,

Ben Haylock

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Applications of model-aware reinforcement learning in Bayesian quantum metrology

Belliardo,

Zoratti,

Giovannetti

2024

Phys. Rev. A

View full text Add to dashboard Cite

Applications of model-aware reinforcement learning in Bayesian quantum metrology

Belliardo,

Zoratti,

Giovannetti

2024

Phys. Rev. A

View full text Add to dashboard Cite

Physics-informed tracking of qubit fluctuations

Berritta,

Krzywda,

Benestad

et al. 2024

Phys. Rev. Applied

View full text Add to dashboard Cite

Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to timescales set by the estimation efficiency. We implement a physics-informed and an adaptive Bayesian estimation strategy and apply them in real time to a semiconductor spin qubit. The physics-informed strategy propagates a probability distribution inside the quantum controller according to the Fokker-Planck equation, appropriate for describing the effects of nuclear spin diffusion in gallium arsenide. Evaluating and narrowing the anticipated distribution by a predetermined qubit probe sequence enables improved dynamical tracking of the uncontrolled magnetic field gradient within the singlet-triplet qubit. The adaptive strategy replaces the probe sequence by a small number of qubit probe cycles, with each probe time conditioned on the previous measurement outcomes, thereby further increasing the estimation efficiency. The combined real-time estimation strategy efficiently tracks low-frequency nuclear spin fluctuations in solid-state qubits, and can be applied to other qubit platforms by tailoring the appropriate update equation to capture their distinct noise sources. Published by the American Physical Society 2024

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Real-time adaptive estimation of decoherence timescales for a single qubit

Cited by 2 publications

References 65 publications

Applications of model-aware reinforcement learning in Bayesian quantum metrology

Applications of model-aware reinforcement learning in Bayesian quantum metrology

Physics-informed tracking of qubit fluctuations

Contact Info

Product

Resources

About