AI summaryⓘ
The authors show that by adding one controllable quantum bit (qubit) to a normal sensor, it becomes much faster to learn information from classical signals, like sound or radio waves. They prove this approach can cut down the number of measurements needed by a huge factor, especially for tasks like finding frequencies or tracking changing signals over time. Using a special quantum device, they demonstrated these big improvements in practice. Their theory, called Quantum Phase-Space Inference (QΨ), helps design these quantum experiments and shows exactly when and why quantum methods give an advantage. This work suggests that even current quantum technology can greatly improve how we gather information from everyday signals.
qubitquantum sensingFourier coefficientssuperconducting cavityquantum phase-space inferencequantum advantagequantum Fisher informationclassical signal processingweak-signal detectiontime-varying signals
Authors
Ishaan Kannan, Sridhar Prabhu, Saeed A. Khan, Mandar M. Sohoni, Xingrui Song, Saswata Roy, Alen Senanian, Valla Fatemi, Peter L. McMahon, Jordan Cotler
Abstract
Quantum technology has the potential to transform scientific discovery, but quantum advantages often require processing capabilities well beyond the reach of experimental platforms. We show that coupling a single controllable qubit to an otherwise conventional sensor can exponentially reduce the number of measurements required to learn classical signals. These rigorous quantum advantages apply to fundamental sensing tasks, including learning Fourier coefficients, extracting temporal correlations from time-varying signals, and estimating transformations of physical observables. Using a superconducting cavity--qubit architecture, we experimentally demonstrate $10^7$-fold reductions in the number of measurements required for Fourier-amplitude and time-varying signal learning. Our $\textit{quantum feature sensing}$ algorithms further enable orders-of-magnitude improvements in simulations of weak-signal dark matter detection and wireless communication applications. These quantum advantages are derived from Quantum Phase-Space Inference (Q$Ψ$), a unifying theory of quantum-enhanced experiments that simultaneously converts a set of experimental objectives and constraints into tight lower bounds and optimal quantum-enhanced learning algorithms while producing a certificate of quantum advantage. Q$Ψ$ extends beyond the regimes captured by quantum Fisher information and provides a framework for systematically identifying rigorous quantum advantages in practical experimental tasks. Together, our results establish that near-term quantum technology can exponentially enhance our ability to learn from classical signals.