论文标题
使用顺序单次测量结果对纯量子状态进行实验性学习
Experimental Learning of Pure Quantum States using Sequential Single-Shot Measurement Outcomes
论文作者
论文摘要
我们通过实验实现一种机器学习方法，以准确识别未知的纯量子状态。该方法称为单杆测量学习，在状态学习和繁殖中实现了$ε= O（n^{ - 1}）$的理论最佳精度，其中$ε$和$ n $表示状态副本的不忠和数量，而无需使用计算范围的层压法。这一优点是由于在学习规则中包含加权随机性的原因，该规则管理了探索各种学习路线的探索。我们通过使用线性播放设置来准备和测量单光子极化量值来实验验证我们方案的优势。实验结果表明，在没有实验参数的情况下，表现出高度准确的状态学习和繁殖，表现出$ O（n^{ - 0.983}）$下降至$ 10^{ - 5} $的$（n^{ - 0.983}）$。
We experimentally implement a machine-learning method for accurately identifying unknown pure quantum states. The method, called single-shot measurement learning, achieves the theoretical optimal accuracy for $ε= O(N^{-1})$ in state learning and reproduction, where $ε$ and $N$ denote the infidelity and number of state copies, without employing computationally demanding tomographic methods. This merit results from the inclusion of weighted randomness in the learning rule governing the exploration of diverse learning routes. We experimentally verify the advantages of our scheme by using a linear-optics setup to prepare and measure single-photon polarization qubits. The experimental results show highly accurate state learning and reproduction exhibiting infidelity of $O(N^{-0.983})$ down to $10^{-5}$, without estimation of the experimental parameters.
