基于量子加法器和遗传算法的量子自编码器
Quantum autoencoders via quantum adders with genetic algorithms
摘要：
量子自动编码器是量子机器学习领域的最新范例，它可以增强量子技术中资源的使用。为此，考虑了内层节点比外层节点少的量子神经网络。在这里，我们提出了一个有用的近似量子加法器和量子自动编码器之间的连接。具体来说，这个链接使我们能够采用优化的近似量子加法器，获得遗传算法，用于实现各种初始状态的量子自动编码器。此外，我们还可以通过遗传算法直接优化量子自编码器。我们的方法为可控量子平台中量子自编码器的设计开辟了一条不同的道路。
英文摘要：
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
