论文标题
使用量子自动编码器校正量子误差
Quantum Error Correction with Quantum Autoencoders
论文作者
论文摘要
主动量子误差校正是实现可靠量子处理器的中心成分。在本文中，我们研究了量子机学习对量子记忆中量子误差校正的潜力。具体而言，我们证明了如何以量子自动编码器的形式训练量子神经网络，以学习有效检测和纠正错误的最佳策略，包括空间相关的计算错误以及量子损失。我们强调说，量子自动编码器的转换功能不仅限于对特定状态的保护，而是扩展到整个逻辑代码。我们还表明，量子神经网络可用于发现最佳适应基础噪声的新逻辑编码。此外，我们发现，即使在量子自动编码器本身中存在中等噪声的情况下，它们仍然可以成功地用于执行有益的量子误差校正，从而延长了逻辑量子的寿命。
Active quantum error correction is a central ingredient to achieve robust quantum processors. In this paper we investigate the potential of quantum machine learning for quantum error correction in a quantum memory. Specifically, we demonstrate how quantum neural networks, in the form of quantum autoencoders, can be trained to learn optimal strategies for active detection and correction of errors, including spatially correlated computational errors as well as qubit losses. We highlight that the denoising capabilities of quantum autoencoders are not limited to the protection of specific states but extend to the entire logical codespace. We also show that quantum neural networks can be used to discover new logical encodings that are optimally adapted to the underlying noise. Moreover, we find that, even in the presence of moderate noise in the quantum autoencoders themselves, they may still be successfully used to perform beneficial quantum error correction and thereby extend the lifetime of a logical qubit.
