Papers
Topics
Authors
Recent
AI Research Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 60 tok/s
Gemini 2.5 Pro 46 tok/s Pro
GPT-5 Medium 14 tok/s Pro
GPT-5 High 15 tok/s Pro
GPT-4o 93 tok/s Pro
Kimi K2 156 tok/s Pro
GPT OSS 120B 441 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Imperfection analyses for random-telegraph-noise mitigation using spectator qubits (2501.15516v1)

Published 26 Jan 2025 in quant-ph

Abstract: Spectator qubits (SQs) for random-telegraph noise mitigation have been proposed by Song et al., Phys. Rev. A, 107, L030601 (2023), where an SQ operates as a noise probe to estimate optimal noise-correction control on the hard-to-access data qubits. It was shown that a protocol with adaptive measurement on the SQs and a Bayesian estimation-based control can suppress the data qubits' decoherence rate by a large factor with quadratic scaling in the SQ sensitivity. However, the protocol's practicality in real-world scenarios remained in question, due to various sources of imperfection that could affect the performance. We therefore analyze here the proposed adaptive protocol under non-ideal conditions, including parameter uncertainties in the system, efficiency and time delay in readout and reset processes of the SQs, and additional decoherence on the SQs. We also explore analytical methods of Bayesian estimation in the time domain and generalize the map-based formalism to non-ideal scenarios. This allows us to derive imperfection bounds at which the decoherence suppression remains approximately the same as under ideal conditions.

Summary

We haven't generated a summary for this paper yet.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.