澳大利亚莫纳什大学L. D. Turner团队研究了压缩量子波形估计。2025年2月24日出版的《物理评论A》杂志发表了这项成果。

量子波形估计（量子传感器对整个时间序列进行采样）有望彻底改变对微弱和随机信号的感知，例如放电神经元发出的生物磁脉冲。对于具有快速瞬态的长持续时间波形，常规量子采样变得令人望而却步，因为它需要许多具有不同控制和读出的测量。

研究组展示了如何仔细选择量子测量，以及压缩感知的现代数学，在远低于奈奎斯特要求的许多测量中实现稀疏信号的量子波形估计。他们用射频修饰的超冷原子感知合成的类神经磁波形，用尽可能少的测量值恢复成功的波形估计，以保证压缩理论边界。

附：英文原文

Title: Compressive quantum waveform estimation

Author: Alex Tritt, Joshua Morris, Christopher C. Bounds, Hamish A. M. Taylor, James Saunderson, L. D. Turner

Issue&Volume: 2025/02/24

Abstract: Quantum waveform estimation, in which quantum sensors sample entire time series, promises to revolutionize the sensing of weak and stochastic signals, such as the biomagnetic impulses emitted by firing neurons. For long duration waveforms with rapid transients, regular quantum sampling becomes prohibitively resource intensive as it demands many measurements with distinct control and readout. In this paper, we demonstrate how careful choice of quantum measurements, along with the modern mathematics of compressive sensing, achieves quantum waveform estimation of sparse signals in a number of measurements far below the Nyquist requirement. We sense synthesized neural-like magnetic waveforms with radio-frequency-dressed ultracold atoms, retrieving successful waveform estimates with as few measurements as compressive theoretical bounds guarantee.

DOI: 10.1103/PhysRevA.111.022625

Source: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.111.022625