Exponential quantum advantage for learning signals with a single qubit

2026-08-13

AI Summary

This work addresses the inefficiency of traditional sensing methods, which require extensive measurements to learn classical signals such as Fourier coefficients or time-varying correlations. The authors propose a quantum feature sensing algorithm that integrates quantum phase-space inference (QΨ) theory, leveraging a single controllable superconducting qubit coupled to the sensor to design an optimal quantum-enhanced learning strategy. This approach achieves, for the first time, an exponential quantum advantage for learning classical signals on near-term quantum devices, supported by rigorous theoretical guarantees. Experimental results demonstrate up to a ten-million-fold (10⁷) reduction in the number of required measurements, yielding substantial performance gains in applications including dark matter detection and wireless communication simulation.

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.

Problem

• quantum advantage
• signal learning
• quantum sensing
• measurement efficiency
• classical signals

Innovation

• quantum feature sensing
• Quantum Phase-Space Inference
• exponential quantum advantage
• single-qubit sensing
• quantum-enhanced learning