Quantum Advantage for Sensing Properties of Classical Fields
Jordan Cotler, Daine L. Danielson, Ishaan Kannan

TL;DR
This paper introduces Quantum Signal Learning (QSL), a quantum-enhanced sensing framework that enables simultaneous estimation of multiple classical signal properties with noise below the vacuum level, outperforming classical methods.
Contribution
The authors propose a novel QSL protocol using two-mode squeezing and passive optics, achieving quantum speedups in classical sensing tasks and introducing an optimal-transport conditioning method.
Findings
Quantum protocol suppresses shot noise below vacuum level.
Exponential speedups over classical strategies in various sensing tasks.
Squeezed light enables exponentially faster sensing of structured backgrounds.
Abstract
Modern precision experiments often probe unknown classical fields with bosonic sensors in quantum-noise-limited regimes where vacuum fluctuations limit conventional readout. We introduce Quantum Signal Learning (QSL), a sensing framework that extends metrology to a broader property-learning setting, and propose a quantum-enhanced protocol that simultaneously estimates many properties of a classical signal with shot noise suppressed below the vacuum level. Our scheme requires only two-mode squeezing, passive optics, and static homodyne measurements, and enables post-hoc classical estimation of many properties from the same experimental dataset. We prove that our protocol enables a quantum speedup for common classical sensing tasks, including measuring electromagnetic correlations, real-time feedback control of interferometric cavities, and Fourier-domain matched filtering. To establish…
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Taxonomy
TopicsMechanical and Optical Resonators · Quantum Information and Cryptography · Cold Atom Physics and Bose-Einstein Condensates
