Monitoring quanta exchanged with a finite phonon reservoir via a quantum dot achieves optimal temperature-sensing precision through Fisher information in the long-time limit.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.
citing papers explorer
-
Optimal current-based sensing of phonon temperature using a finite reservoir
Monitoring quanta exchanged with a finite phonon reservoir via a quantum dot achieves optimal temperature-sensing precision through Fisher information in the long-time limit.
-
Getting large-scale quantum neural networks ready for quantum hardware
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.