论文标题
通过可扩展的神经网络监督随机量子电路的学习
Supervised learning of random quantum circuits via scalable neural networks
论文作者
论文摘要
预测量子电路的输出是一项硬计算任务，在通用量子计算机的开发中起着关键作用。在这里，我们研究了随机量子电路的输出期望值的监督学习。深层卷积神经网络（CNN）经过训练，可以使用经典模拟电路的数据库来预测单量和两数quit的期望值。这些电路是通过适当设计的组成门编码的。分析了以前看不见的电路的预测准确性，还可以与免费的IBM量子程序可用的小规模量子计算机进行比较。根据网络深度和训练集大小，CNN通常优于量子设备，具体取决于电路深度。值得注意的是，我们的CNN被设计为可扩展。这使我们可以利用转移学习和执行外推到比培训集中包含的电路更大的电路。这些CNN还表现出对噪声的显着弹性，即，即使在很少的测量值中对（模拟）期望值进行了训练，它们仍然是准确的。
Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are represented via an appropriately designed one-hot encoding of the constituent gates. The prediction accuracy for previously unseen circuits is analyzed, also making comparisons with small-scale quantum computers available from the free IBM Quantum program. The CNNs often outperform the quantum devices, depending on the circuit depth, on the network depth, and on the training set size. Notably, our CNNs are designed to be scalable. This allows us exploiting transfer learning and performing extrapolations to circuits larger than those included in the training set. These CNNs also demonstrate remarkable resilience against noise, namely, they remain accurate even when trained on (simulated) expectation values averaged over very few measurements.
