用于计算的量子传感器：利用超导量子比特的量子计算磁场传感
Quantum sensors that compute: quantum computational magnetic-field sensing using a superconducting qubit
- School of Applied and Engineering Physics, Cornell University（康奈尔大学应用工程物理学院）
- Department of Physics, Cornell University（康奈尔大学物理系）
- Diraq
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（麻省理工学院电气工程和计算机科学系）
- Research Laboratory of Electronics, Massachusetts Institute of Technology（麻省理工学院电子研究实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过实验演示了基于同一超导transmon量子比特的量子计算传感，其在静态和振荡磁场的分类任务中，相较于常规基线实现了更高的准确率，验证了量子计算对量子传感的增强作用。
AI中文摘要：
对单量子比特量子传感器的测量最多可获取被传感信号的1比特信息。针对该信号执行分类任务时，常规方法是多次重复传感协议，对测量结果取平均以获得被传感信号的高精度估计值，随后应用经典后处理。量子计算传感（Quantum Computational Sensing, QCS）是一种替代方法，它打破了“先获取信号的经典估计值，再在后处理中对该估计值执行函数计算”的范式。QCS将量子传感与量子计算相结合，把与任务相关的信号信息浓缩到每次测量所揭示的唯一比特中。在此，我们报告QCS的实验演示，其中传感和计算均由同一个超导transmon量子比特完成。我们考虑基于磁通线电流感应的静态和振荡磁场的各类二分类任务，这些磁场通过作为量子比特一部分的双结超导量子干涉器件（Superconducting Quantum Interference Device, SQUID）回路被传感。我们采用基于量子信号处理的协议，在测量前于量子域中对被传感信号进行预处理。对于静态磁场任务，我们的协议性能优于基于Ramsey相位估计的常规基线，幅度达15个百分点；对于振荡磁场任务，与基于优化动力学去耦协议的常规基线相比，我们对信号振幅和频率的分类准确率分别高出达20和15个百分点。我们的结果表明，即使在受退相干和易出错操作等实际限制的最小规模量子系统中，量子计算也能提升量子传感性能。
英文摘要：
A measurement of a single-qubit quantum sensor reveals at most 1 bit of information about the signal that was sensed. To perform a classification task on the signal, the conventional approach is to repeat a sensing protocol many times, averaging the measurement results to obtain a high-precision estimate of the sensed signal, and then to apply classical postprocessing. Quantum computational sensing (QCS) is an alternative approach that breaks with the paradigm of first obtaining a classical estimate of the signal and then computing a function of the estimated signal in postprocessing. QCS instead combines quantum sensing with quantum computing to concentrate information about the signal relevant to the task into the solitary bit revealed by each measurement. Here, we report on the experimental demonstration of QCS where sensing and computing were both performed by the same single superconducting transmon qubit. We consider various binary classification tasks based on static and oscillating magnetic fields induced by current through a flux line. The fields were sensed through a double-junction superconducting quantum interference device (SQUID) loop that was part of the qubit. We used a protocol based on quantum signal processing to preprocess the sensed signals in the quantum domain prior to measurement. For tasks on static magnetic fields, our protocol outperformed the conventional baseline of Ramsey-based phase estimation by as much as 15 percentage points. For tasks on oscillating magnetic fields, we classified signal amplitude and frequency with up to 20 and 15 percentage points higher accuracy, respectively, compared to the conventional baseline of optimized dynamical-decoupling protocols. Our results illustrate how quantum computing can enhance quantum sensing even with a minimally sized quantum system subject to the practical limitations of decoherence and error-prone operations.
