Quick Navigation
Topics
Trapped Ion Quantum Computing
Precision limits for time-dependent quantum metrology under Markovian noise
arXiv
Authors: Luca Previdi, Francesco Albarelli
Year
2026
Paper ID
63802
Status
Preprint
Abstract Read
~2 min
Abstract Words
164
Citations
N/A
Abstract
We derive ultimate precision bounds for estimating parameters encoded in time-dependent Hamiltonians in the presence of general Markovian noise, allowing for arbitrary adaptive protocols with fast controls and noiseless ancillas. Extending the minimization-over-purifications framework to time-varying continuous channels, we obtain a differential upper bound on the achievable quantum Fisher information (QFI) that can be evaluated at all times via semidefinite programming. For parameter-independent noise, we prove a universal long-time scaling law: if the coherent (noiseless) dynamics yields Qcoh(T)sim T2k, then under Markovian noise the QFI scales at most as Q(T)sim T2k in the DHNLS regime, whereas in the DHLS regime it is fundamentally limited to Q(T)sim T2k-1. We illustrate these behaviors on paradigmatic driven-qubit sensors, exhibiting T4 and T3 scalings under dephasing and spontaneous emission, respectively. Finally, we provide explicit continuous exact and approximate quantum error correction constructions - supplemented by spin-squeezed probes - that asymptotically saturate the bounds, establishing their tightness.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- We derive ultimate precision bounds for estimating parameters encoded in time-dependent Hamiltonians in the presence of general Markovian noise, allowing for arbitrary adaptive...
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.