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.
翻译:最近许多机器学习任务都采用量子计算来提高分类精确度和培训效率,办法是利用量子机械学(QML)来利用量子机械学(QML)来提高数据精确度和培训效率。变量量电路(VQC)经常用来建立一个量子神经网络(QNN),这是传统神经网络的对应物量网络。但是,由于硬件的限制,目前的量子设备只能允许一个人使用少量量子计算来代表数据和进行简单的量子计算。单量子装置的量子资源有限,会降低数据使用量子电路的比例,限制量子电路的规模,从而在某种程度上防止量子优势。为了减轻这一限制,我们建议采用一种方法,通过合作使用多个小型量子量子量子网络的量子网络(QQQNN)来建立一个可缩放量子神经网络(SQNNN),这样可以灵活地使用可缩放量子的量子量子网络(SQNNN)来改进定期量子网络。在S的量子系统上,可以灵活地使用一个量子系统进行量子级的量子分析。在S的量子系统上,用量子系统进行量子的量子的量子的量子分析,在S的量子系统上,可以显示一个量子的量子的量子的量子的量子的量子系统,用量子的量子的量子的量子系统,用量子的量子系统,可以进行量子的量子的量子的量子仪器可以进行。