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Peer-reviewedCC BY 4.0Quantum

Optimal phase estimation in the presence of correlated dephasing

Abstract

We investigate optimal metrological protocols for phase estimation in the presence of correlated dephasing noise, including spin-squeezed state sensing strategies as well as parallel and adaptive protocols optimized using tensor-network-based numerical methods. The results are benchmarked against fundamental bounds obtained either via the latest quantum comb extension method or an optimized classical simulation method. We find that the spin-squeezed states offer practically optimal performance in the regime where phase fluctuations are positively correlated but can be outperformed by tensor-network-optimized strategies for negatively correlated fluctuations.

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BibTeX
@article{ghosh2026optimal,
  title = {{Optimal phase estimation in the presence of correlated dephasing}},
  author = {Srijon Ghosh and Arkadiusz Kobus and Stanisław Kurdziałek and Rafał Demkowicz-Dobrzański},
  journal = {Quantum},
  year = {2026},
  month = oct,
  doi = {10.22331/q-2026-10-28-2251},
  url = {https://quantum-journal.org/papers/q-2026-10-28-2251/},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Optimal phase estimation in the presence of correlated dephasing
AU  - Srijon Ghosh
AU  - Arkadiusz Kobus
AU  - Stanisław Kurdziałek
AU  - Rafał Demkowicz-Dobrzański
PY  - 2026
DA  - 2026/10/28
JO  - Quantum
DO  - 10.22331/q-2026-10-28-2251
UR  - https://quantum-journal.org/papers/q-2026-10-28-2251/
AB  - We investigate optimal metrological protocols for phase estimation in the presence of correlated dephasing noise, including spin-squeezed state sensing strategies as well as parallel and adaptive protocols optimized using tensor-network-based numerical methods. The results are benchmarked against fundamental bounds obtained either via the latest quantum comb extension method or an optimized classical simulation method. We find that the spin-squeezed states offer practically optimal performance in the regime where phase fluctuations are positively correlated but can be outperformed by tensor-network-optimized strategies for negatively correlated fluctuations.
SN  - 2521-327X
ER  -