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.
翻译:预测量子电路的输出是一项艰难的计算任务,在开发通用量子计算机方面发挥着关键作用。 我们在这里调查随机量子电路输出预期值的监督学习情况。 深演神经网络(CNNs)经过培训,使用传统模拟电路数据库预测单平方位值和二平方位预期值。 这些电路通过构件门适当设计的单热编码代表。 分析先前看不见电路的预测准确性, 并与自由的IBM 量子程序提供的小型量子计算机进行比较。 CNNs往往超过量子设备, 取决于电路深度、 网络深度和训练的尺寸。 值得注意的是, 我们的CNNs的设计是可扩缩的。 这使我们能够利用传输学习和对比培训集中的大得多的电路进行外推。 这些CNNS还表现出惊人的抵御噪音的能力, 也就是说, 即使在( 模拟) 平均测量值培训时, 它们仍然准确无误。