论文标题
可以使用经典阴影进行经典优化的交替分层量子电路
Alternating Layered Variational Quantum Circuits Can Be Classically Optimized Efficiently Using Classical Shadows
论文作者
论文摘要
变异量子算法（VQA）是经典神经网络（NNS）的量子类似物。 VQA由一个参数化的量子电路（PQC）组成，该量子电路（PQC）由Ansatzes多层（较简单的PQC，是NN层的类比）组成，仅在参数的选择中有所不同。先前的工作已经确定了交替的分层ANSATZ可能是近期量子计算中的新标准ANSATZ。确实，浅层交替的分层VQA易于实施，并且已被证明是可训练的和表现力的。在这项工作中，我们引入了一种培训算法，以降低此类VQA的培训成本的指数降低。此外，我们的算法使用量子输入数据的经典阴影，因此可以在具有严格性能保证的经典计算机上运行。我们证明了使用算法在寻找状态准备电路和量子自动编码器的示例问题的培训成本方面的2--3个数量级提高。
Variational quantum algorithms (VQAs) are the quantum analog of classical neural networks (NNs). A VQA consists of a parameterized quantum circuit (PQC) which is composed of multiple layers of ansatzes (simpler PQCs, which are an analogy of NN layers) that differ only in selections of parameters. Previous work has identified the alternating layered ansatz as potentially a new standard ansatz in near-term quantum computing. Indeed, shallow alternating layered VQAs are easy to implement and have been shown to be both trainable and expressive. In this work, we introduce a training algorithm with an exponential reduction in training cost of such VQAs. Moreover, our algorithm uses classical shadows of quantum input data, and can hence be run on a classical computer with rigorous performance guarantees. We demonstrate 2--3 orders of magnitude improvement in the training cost using our algorithm for the example problems of finding state preparation circuits and the quantum autoencoder.
