论文标题
使用量子机学习优化高效量子存储器的近期量子设备
Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices
论文作者
论文摘要
量子记忆是任何全球尺度量子互联网，高性能量子网络和近期量子计算机的基本基础。量子记忆的一个主要问题是量子系统从量子存储器的量子寄存器中量子系统的检索效率较低。在这里，我们为近期量子设备定义了一种称为高返回效率（HRE）量子记忆的新型量子存储器。 HRE量子存储器单元在其硬件级别上集成了本地统一操作，以优化读取过程，并利用量子机学习的高级技术。我们定义了HRE量子存储器的集成统一操作，证明了学习过程，并评估可实现的输出信噪比值。我们证明，无需使用任何标记的数据或训练序列，以无监督的方式实现了HRE量子存储器的本地单位。我们表明，HRE量子存储器的读数过程以完全盲目的方式实现，而没有任何有关输入量子系统的信息或量子寄存器的未知量子操作。我们评估了HRE量子存储器和输出SNR的检索效率（信噪比）。对于登机口量子计算机和量子互联网的近期量子设备而言，结果特别方便。
Quantum memories are a fundamental of any global-scale quantum Internet, high-performance quantum networking and near-term quantum computers. A main problem of quantum memories is the low retrieval efficiency of the quantum systems from the quantum registers of the quantum memory. Here, we define a novel quantum memory called high-retrieval-efficiency (HRE) quantum memory for near-term quantum devices. An HRE quantum memory unit integrates local unitary operations on its hardware level for the optimization of the readout procedure and utilizes the advanced techniques of quantum machine learning. We define the integrated unitary operations of an HRE quantum memory, prove the learning procedure, and evaluate the achievable output signal-to-noise ratio values. We prove that the local unitaries of an HRE quantum memory achieve the optimization of the readout procedure in an unsupervised manner without the use of any labeled data or training sequences. We show that the readout procedure of an HRE quantum memory is realized in a completely blind manner without any information about the input quantum system or about the unknown quantum operation of the quantum register. We evaluate the retrieval efficiency of an HRE quantum memory and the output SNR (signal-to-noise ratio). The results are particularly convenient for gate-model quantum computers and the near-term quantum devices of the quantum Internet.
