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Supervised learning of random quantum circuits via scalable neural networks. (arXiv:2206.10348v2 [quant-ph] UPDATED)
cs.LG updates on arXiv.org arxiv.org
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, …
arxiv learning networks neural networks quantum random scalable supervised learning