School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore.
School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore.
Neural Netw. 2021 Apr;136:141-151. doi: 10.1016/j.neunet.2021.01.003. Epub 2021 Jan 12.
Quantum information processing tasks require exotic quantum states as a prerequisite. They are usually prepared with many different methods tailored to the specific resource state. Here we provide a versatile unified state preparation scheme based on a driven quantum network composed of randomly-coupled fermionic nodes. The output of such a system is then superposed with the help of linear mixing where weights and phases are trained in order to obtain desired output quantum states. We explicitly show that our method is robust and can be utilized to create almost perfect maximally entangled, NOON, W, cluster, and discorded states. Furthermore, the treatment includes energy decay in the system as well as dephasing and depolarization. Under these noisy conditions we show that the target states are achieved with high fidelity by tuning controllable parameters and providing sufficient strength to the driving of the quantum network. Finally, in very noisy systems, where noise is comparable to the driving strength, we show how to concentrate entanglement by mixing more states in a larger network.
量子信息处理任务需要奇异的量子态作为前提。它们通常使用许多不同的方法来制备，这些方法针对特定的资源状态进行了定制。在这里，我们提供了一种基于由随机耦合费米子节点组成的驱动量子网络的通用统一状态制备方案。然后，借助线性混合来叠加这样一个系统的输出，其中权重和相位被训练以获得所需的输出量子态。我们明确表明，我们的方法是鲁棒的，可以用来创建几乎完美的最大纠缠态、NOON 态、W 态、簇态和非相干态。此外，该处理还包括系统中的能量衰减以及退相和去极化。在这些噪声条件下，我们通过调整可控参数并为量子网络的驱动提供足够的强度，证明了目标态可以以高保真度实现。最后，在噪声非常大的系统中，其中噪声与驱动强度相当，我们展示了如何通过在更大的网络中混合更多的状态来集中纠缠。
