[论文] Exponential quantum advantage for learning signals with a single qubit

论文概要

研究领域: ML
作者: Ishaan Kannan, Sridhar Prabhu, Saeed A. Khan, Mandar M. Sohoni, Xingrui Song, Saswata Roy, Alen Senanian, Valla Fatemi, Peter L. McMahon, Jordan Cotler
发布时间: 2026-08-13
arXiv: 2608.13521

中文摘要

量子技术有潜力改变科学发现，但量子优势通常需要远超实验平台能力的处理能力。我们证明，将单个可控量子比特耦合到常规传感器上，可以指数级减少学习经典信号所需的测量次数。这些严格的量子优势适用于基础感知任务，包括学习傅里叶系数、从时变信号中提取时间相关性以及估计物理可观测量的变换。使用超导腔-量子比特架构，我们在实验中展示了傅里叶幅度和时变信号学习所需测量次数的10^7倍减少。我们的量子特征感知算法进一步在弱信号暗物质检测和无线通信应用的模拟中实现了数量级的改进。这些量子优势源自量子相空间推断（QΨ），一种统一理论，将量子增强实验同时转化为一组实验目标和约束的紧下界和最优量子增强学习算法，同时产生量子优势证书。QΨ超越了量子Fisher信息捕获的范围，为系统识别实际实验任务中的严格量子优势提供了框架。总之，我们的结果确立了近期量子技术可以指数级增强我们从经典信号中学习的能力。

原文摘要

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 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 (QΨ), a unifying theory of quantum-enhanced experiments that simultaneously converts a set of experimental objectives and约束into tight lower bounds and optimal quantum-enhanced learning algorithms while producing a certificate of quantum advantage. QΨ extends beyond the regimes captured by quantum Fisher information and provides a framework for systematically识别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.

自动采集于 2026-08-15

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