research
Physics Reconstructed the Samples the Sensor Never Took
Positive-semidefinite, Toeplitz and low-rank constraints improved sparse quantum-sensing estimates in simulation.
Summary
Positive-semidefinite, Toeplitz and low-rank constraints improved sparse quantum-sensing estimates in simulation.
A convex reconstruction method uses universal structure in time-domain correlation functions. In simulated GHZ magnetometry it reduced frequency error in data-starved settings without adding hardware, though it did not generally reach the shot-noise limit.
Why it matters
Positive-semidefinite, Toeplitz and low-rank constraints improved sparse quantum-sensing estimates in simulation.
Limits and context
- In simulated GHZ magnetometry it reduced frequency error in data-starved settings without adding hardware, though it did not generally reach the shot-noise limit.
Key claims
Positive-semidefinite, Toeplitz and low-rank constraints improved sparse quantum-sensing estimates in simulation.
Qualification: In simulated GHZ magnetometry it reduced frequency error in data-starved settings without adding hardware, though it did not generally reach the shot-noise limit.
Evidence: source-2026-08-12-020
Sources
- arXiv preprint 2608.11092arXiv · primary research
Corrections
No corrections have been recorded for this story.