Many recent machine learning tasks resort to quantum computing to improve
classification accuracy and training efficiency by taking advantage of quantum
mechanics, known as quantum machine learning (QML). The variational quantum
circuit (VQC) is frequently utilized to build a quantum neural network (QNN),
which is a counterpart to the conventional neural network. Due to hardware
limitations, however, current quantum devices only allow one to use few qubits
to represent data and perform simple quantum computations. The limited quantum
resource on a single quantum device degrades the data usage and limits the
scale of the quantum circuits, preventing quantum advantage to some extent. To
alleviate this constraint, we propose an approach to implementing a scalable
quantum neural network (SQNN) by utilizing the quantum resource of multiple
small-size quantum devices cooperatively. In an SQNN system, several quantum
devices are used as quantum feature extractors, extracting local features from
an input instance in parallel, and a quantum device works as a quantum
predictor, performing prediction over the local features collected through
classical communication channels. The quantum feature extractors in the SQNN
system are independent of each other, so one can flexibly use quantum devices
of varying sizes, with larger quantum devices extracting more local features.
Especially, the SQNN can be performed on a single quantum device in a modular
fashion. Our work is exploratory and carried out on a quantum system simulator
using the TensorFlow Quantum library. The evaluation conducts a binary
classification on the MNIST dataset. It shows that the SQNN model achieves a
comparable classification accuracy to a regular QNN model of the same scale.
Furthermore, it demonstrates that the SQNN model with more quantum resources
can significantly improve classification accuracy.

本研究探讨了一种可扩展的量子神经网络（SQNN）方法，通过利用多个小型量子设备的量子资源来提高分类精度和训练效率。 对 MNIST 数据集进行的评估表明，相比于相同规模的常规 QNN 模型， SQNN 模型可以实现具有可比较的分类精度，并且超过一定量子资源的 SQNN 模型可以显著提高分类精度。