论文标题
找到使用自动编码器神经网络的纠缠检测的半最佳测量结果
Finding semi-optimal measurements for entanglement detection using Autoencoder Neural Networks
论文作者
论文摘要
纠缠是量子信息科学的关键资源之一，它可以鉴定出对广泛的量子技术和现象必不可少的纠缠状态。但是，这个问题在计算和实验上都具有挑战性。在这里，我们使用自动编码器神经网络来找到半最佳的不完整测量值，这些测量最有用，以检测纠缠状态。我们表明，可以找到具有三个测量值的高性能纠缠探测器。同样，借助国家的完整信息，我们开发了一个神经网络，该神经网络几乎可以完美地识别所有两个问题的纠缠状态。该结果为使用机器学习技术自动开发有效的纠缠证人和纠缠检测铺平了道路。
Entanglement is one of the key resources of quantum information science which makes identification of entangled states essential to a wide range of quantum technologies and phenomena. This problem is however both computationally and experimentally challenging. Here we use autoencoder neural networks to find semi-optimal set of incomplete measurements that are most informative for the detection of entangled states. We show that it is possible to find high-performance entanglement detectors with as few as three measurements. Also, with the complete information of the state, we develop a neural network that can identify all two-qubits entangled states almost perfectly. This result paves the way for automatic development of efficient entanglement witnesses and entanglement detection using machine learning techniques.
