论文标题
从随机基准测试中对平均非马克维亚性的机器学习
Machine Learning of Average Non-Markovianity from Randomized Benchmarking
论文作者
论文摘要
随着量子设备的尺寸和深度不断增长，嘈杂的量子电路中相关性的存在将是不可避免的副作用。随机基准测试（RB）可以说是最初评估量子设备总体性能的最简单方法，并确定了时间相关的存在，即所谓的非马克维亚性；但是，当检测到这种存在时，迄今为止，它仍然是量化其特征的挑战。在这里，我们展示了一种方法，该方法利用了Matrix产品操作员利用机器学习的能力来推断RB实验数据显示的最小平均非马克维亚性，认为这可以实现任何合适的门集，并针对大多数特定的Polpose RB Techniques量身定制。
The presence of correlations in noisy quantum circuits will be an inevitable side effect as quantum devices continue to grow in size and depth. Randomized Benchmarking (RB) is arguably the simplest method to initially assess the overall performance of a quantum device, as well as to pinpoint the presence of temporal-correlations, so-called non-Markovianity; however, when such presence is detected, it hitherto remains a challenge to operationally quantify its features. Here, we demonstrate a method exploiting the power of machine learning with matrix product operators to deduce the minimal average non-Markovianity displayed by the data of a RB experiment, arguing that this can be achieved for any suitable gate set, as well as tailored for most specific-purpose RB techniques.
