论文标题
通过遗传算法探索嘈杂量子通道相干信息的超级添加性
Exploring super-additivity of coherent information of noisy quantum channels through Genetic algorithms
论文作者
论文摘要
机器学习技术越来越多地用于基础研究中，以解决各种具有挑战性的问题。在这里，我们探索了一种这样的技术，可以解决量子通信方案中的一个重要问题。在通过嘈杂的量子通道传输量子信息的同时，该通道的效用的特征是其量子能力。但是，量子通道显示了一个有趣的属性，称为相干信息的超级添加性。这使得量子能力的计算成为涉及指数增加搜索空间优化的严重计算问题。在这项工作中，我们首先使用神经网络ANSATZ代表量子状态，然后应用进化优化方案来解决此问题。我们在Qubit Pauli通道的三参数空间中发现了区域，其中连贯的信息表现出这种超级增强功能。我们表征了获得高相干信息的量子代码，找到了几种非平凡的量子代码，这些代码的表现优于某些Pauli通道的重复代码。对于某些Pauli通道，这些代码显示出非常高的0.01阶的超级添加性，远高于其他经过良好研究的量子通道中观察到的值。我们进一步比较了神经网络ANSATZ和原始ANSATZ的学习性能，以发现在三弹性情况下，神经网络ANSATZ在查找高相干信息的量子代码方面优于原始表示。我们还将进化算法的学习性能与简单的粒子群优化方案进行了比较，并显示了表明可比性能的经验结果，这表明神经网络ANSATZ与进化方案相结合确实是一种有希望的方法，可以找到高相干信息的非平凡量子代码。
Machine learning techniques are increasingly being used in fundamental research to solve various challenging problems. Here we explore one such technique to address an important problem in quantum communication scenario. While transferring quantum information through a noisy quantum channel, the utility of the channel is characterized by its quantum capacity. Quantum channels, however, display an intriguing property called super-additivity of coherent information. This makes the calculation of quantum capacity a hard computational problem involving optimization over an exponentially increasing search space. In this work, we first utilize a neural network ansatz to represent quantum states and then apply an evolutionary optimization scheme to address this problem. We find regions in the three-parameter space of qubit Pauli channels where coherent information exhibits this super-additivity feature. We characterised the quantum codes that achieves high coherent information, finding several non-trivial quantum codes that outperforms the repetition codes for some Pauli channels. For some Pauli channels, these codes displays very high super-additivity of the order of 0.01, much higher than the observed values in other well studied quantum channels. We further compared the learning performance of the Neural Network ansatz with the raw ansatz to find that in the three-shot case, the neural network ansatz outperforms the raw representation in finding quantum codes of high coherent information. We also compared the learning performance of the evolutionary algorithm with a simple Particle Swarm Optimisation scheme and show empirical results indicating comparable performance, suggesting that the Neural Network ansatz coupled with the evolutionary scheme is indeed a promising approach to finding non-trivial quantum codes of high coherent information.
