论文标题
基于神经网络的量子检测参数估计
Neural-network-based parameter estimation for quantum detection
论文作者
论文摘要
人工神经网络通过大约编码将它们关联的函数桥接到输出结果中。这是在训练网络的收集集合之后实现的，结果导致了神经元连接和偏见的调整。在量子检测方案的背景下，神经网络找到了自然的游乐场。特别是，在存在目标的情况下，量子传感器会提供响应，即输入数据，随后可以由输出目标特征的神经网络处理。我们证明，在量子传感器提出复杂响应以及由于测量数量减少而导致的大量射击噪声下，经过足够训练的神经网络能够以最少了解基础物理模型的了解来表征目标。我们用$^{171} $ yb $^{+} $原子传感器的开发来体现该方法。但是，我们的协议是一般的，因此适用于任意量子检测方案。
Artificial neural networks bridge input data into output results by approximately encoding the function that relates them. This is achieved after training the network with a collection of known inputs and results leading to an adjustment of the neuron connections and biases. In the context of quantum detection schemes, neural networks find a natural playground. In particular, in the presence of a target, a quantum sensor delivers a response, i.e., the input data, which can be subsequently processed by a neural network that outputs the target features. We demonstrate that adequately trained neural networks enable to characterize a target with minimal knowledge of the underlying physical model, in regimes where the quantum sensor presents complex responses, and under a significant shot noise due to a reduced number of measurements. We exemplify the method with a development for $^{171}$Yb$^{+}$ atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.
