论文标题
可重新配置的噪声量子网络上的量子状态歧视
Quantum State Discrimination on Reconfigurable Noise-Robust Quantum Networks
论文作者
论文摘要
量子信息处理中的一个基本问题是系统的一组量子状态之间的歧视。在本文中，我们在图形描述的开放量子系统上解决了这个问题，该量子系统的进化是由量子随机步行定义的。特别地，图形的结构模仿了神经网络的结构，量子状态以在输入节点上进行歧视，并在输出节点上获得歧视。我们优化网络的参数，以获得正确歧视的最高概率。数值模拟表明，在短暂的时间后，正确决策的概率接近理论最佳量子限制。这些结果在分析中对小图进行了分析确认。最后，我们分析了网络对不同量子状态集的鲁棒性和可重构性，并表明该体系结构可以为我们的协议实验实现以及深入学习的新颖量子概括铺平道路。
A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system. In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Stochastic Walk. In particular, the structure of the graph mimics those of neural networks, with the quantum states to discriminate encoded on input nodes and with the discrimination obtained on the output nodes. We optimize the parameters of the network to obtain the highest probability of correct discrimination. Numerical simulations show that after a transient time the probability of correct decision approaches the theoretical optimal quantum limit. These results are confirmed analytically for small graphs. Finally, we analyze the robustness and reconfigurability of the network for different set of quantum states, and show that this architecture can pave the way to experimental realizations of our protocol as well as novel quantum generalizations of deep learning.
