发表机构
School of Computer and Artificial Intelligence, Southwest Minzu University; Centre for Quantum Technologies, National University of Singapore; School of Physics, Nankai University; MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit; National Institute of Education, Nanyang Technological University（西南民族大学计算机与人工智能学院; 新加坡国立大学量子技术中心; 南开大学物理学院; MajuLab，CNRS-UNS-NUS-NTU国际联合研究单位; 南洋理工大学国立教育学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究推导了退相干增强量子神经元Fisher信息率的经典界，表明量子相干性无法超越经典两态过程的优化吞吐量，为量子神经处理提供硬件感知基准。
AI 中文摘要
退相干可以锐化耗散量子神经元的激活，但稳态响应并不能决定其输出每单位时间传递多少输入信息。对于以速率ν进行部分重置更新的单个量子比特，我们推导了阈值响应带宽以及从重复投影读出（包括其反作用）的完整记录的精确局部Fisher信息率。在激活阈值附近，该率满足R≤ν(1-s)(1+s)^2/2≤16ν/27，其中s是每次更新的保留振幅。对于每个固定的有限马尔可夫退相干速率，对读出间隔和s的优化给出相同的上确界，在s=1/3且理想读出任意快时逼近该上确界。有限的测量死区时间反而选择了一个有限的最优间隔，我们通过无量纲的两参数优化获得该间隔。理想界由经典的两态跳跃过程饱和：量子相干性改变了有限节奏的性能，但不能提高优化后的界。这些结果将噪声增强的激活与量子神经元的操作信息吞吐量区分开来，并为有限时间量子神经处理提供了硬件感知的基准。
英文摘要
Dephasing can sharpen the activation of a dissipative quantum neuron, but a stationary response does not determine how much input information its output delivers per unit time. For a single qubit with partial-reset updates at rate $ν$, we derive the threshold response bandwidth and the exact local Fisher information rate of the complete record from repeated projective readouts, including their backaction. Near the activation threshold, the rate obeys $\mathcal R\leqν(1-s)(1+s)^2/2\leq16ν/27$, where $s$ is the retained amplitude of each update. For every fixed finite Markovian dephasing rate, optimization over the readout interval and $s$ gives the same supremum, approached at $s=1/3$ with arbitrarily rapid ideal readout. A finite measurement dead time instead selects a finite optimal interval, which we obtain by a dimensionless two-parameter optimization. The ideal bound is saturated by a classical two-state jump process: quantum coherence changes finite-cadence performance but cannot raise the optimized bound. These results separate noise-enhanced activation from the operational information throughput of a quantum neuron and provide a hardware-aware benchmark for finite-time quantum neural processing.
