In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measuring the output state. This work studies applications of variational quantum circuits and machine learning techniques for discriminating such channels. In particular, we explore (i) the practical implementation of embedding this task into the framework of variational quantum computing, (ii) training a quantum classifier based on variational quantum circuits, and (iii) applying the quantum kernel estimation technique. For testing these three channel discrimination approaches, we considered a pair of entanglement-breaking channels and the depolarizing channel with two different depolarization factors. For the approach (i), we address solving the quantum channel discrimination problem using widely discussed parallel and sequential strategies. We show the advantage of the latter in terms of better convergence with less quantum resources. Quantum channel discrimination with a variational quantum classifier (ii) allows one to operate even with random and mixed input states and simple variational circuits. The kernel-based classification approach (iii) is also found effective as it allows one to discriminate depolarizing channels associated not with just fixed values of the depolarization factor, but with ranges of it. Additionally, we discovered that a simple modification of one of the commonly used kernels significantly increases the efficiency of this approach. Finally, our numerical findings reveal that the performance of variational methods of channel discrimination depends on the trace of the product of the output states. These findings demonstrate that quantum machine learning can be used to discriminate channels, such as those representing physical noise processes.
翻译:在量子频道歧视问题中,我们区分了一定数量的量子频道,这是通过一个频道发送输入状态和测量输出状态来完成的。本项工作研究变量量子电路和机器学习技术的应用,以区别这些渠道。特别是,我们探讨了(一) 将这一任务嵌入变量量子计算框架的实际实施情况,(二) 以量子电路为基础培训量子分类器,(三) 应用量子内核估计技术。在测试这三个频道差异分析方法时,我们认为,一对相错的断流渠道和分解渠道,同时使用两种不同的物理分解因素。对于这一方法,我们用广泛讨论的平行和顺序战略来解决量子频道歧视问题。我们展示了后者的优势,即与量子电路流的更好趋同,让一个人使用随机和混合输入状态和简单的变异电路进行操作。基于内分流的分类方法也发现,它允许一种分流渠道的分解结果,而我们使用的一种分解的分流的分化方法,即我们所发现,我们所发现,最终使用的分解的分流的分流的分流的分流的分流的分化方法,我们使用的分解的分流的分流的分流的分流的分流的分流的分流方法,我们使用的分解了。