Model compression, such as pruning and quantization, has been widely applied
to optimize neural networks on resource-limited classical devices. Recently,
there are growing interest in variational quantum circuits (VQC), that is, a
type of neural network on quantum computers (a.k.a., quantum neural networks).
It is well known that the near-term quantum devices have high noise and limited
resources (i.e., quantum bits, qubits); yet, how to compress quantum neural
networks has not been thoroughly studied. One might think it is straightforward
to apply the classical compression techniques to quantum scenarios. However,
this paper reveals that there exist differences between the compression of
quantum and classical neural networks. Based on our observations, we claim that
the compilation/traspilation has to be involved in the compression process. On
top of this, we propose the very first systematical framework, namely CompVQC,
to compress quantum neural networks (QNNs).In CompVQC, the key component is a
novel compression algorithm, which is based on the alternating direction method
of multipliers (ADMM) approach. Experiments demonstrate the advantage of the
CompVQC, reducing the circuit depth (almost over 2.5 %) with a negligible
accuracy drop (<1%), which outperforms other competitors. Another promising
truth is our CompVQC can indeed promote the robustness of the QNN on the
near-term noisy quantum devices.

本文介绍了 CompVQC 框架，一种量子神经网络（QNN）压缩的系统性方法，使用交替方向乘子法（ADMM）的压缩算法，展示了 CompVQC 在减少电路深度（超过 2.5%）且精度损失极小（<1%）方面的优越性，另外 CompVQC 也提高了 QNN 在噪声量子设备上的鲁棒性。