用单个量子比特学习信号的指数级量子优势
Exponential quantum advantage for learning signals with a single qubit
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中文总结 AI 辅助
研究发现用单个可控量子比特耦合常规传感器，可指数级减少经典信号学习的测量次数，基于量子相空间推理的算法在相关任务中实现10^7倍测量缩减，为近期量子技术增强信号学习提供依据。
中文摘要 AI 辅助
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所能达到的处理能力。我们证明，将单个可控量子比特与常规传感器耦合，可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传感任务，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。我们利用超导腔-量子比特架构，实验证明傅里叶振幅和时变信号学习所需的测量次数减少了10^7倍。我们的量子特征感知（quantum feature sensing）算法还能在弱信号暗物质探测和无线通信应用的模拟中实现数量级的改进。这些量子优势源于量子相空间推理（Quantum Phase-Space Inference, QΨ），这是一种量子增强实验的统一理论，可同时将一组实验目标和约束转化为紧下界和最优量子增强学习算法，同时生成量子优势的证明。QΨ超越了量子费舍尔信息所涵盖的范围，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，我们的结果表明，近期量子技术可指数级增强我们从经典信号中学习的能力。
英文摘要
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 $\textit{quantum feature sensing}$ 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.
发表机构
- Harvard University（哈佛大学）
- Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。
