# Scalable Quantum Neural Networks for Classification

@article{Wu2022ScalableQN, title={Scalable Quantum Neural Networks for Classification}, author={Jindi Wu and Zeyi Tao and Qun Li}, journal={ArXiv}, year={2022}, volume={abs/2208.07719} }

—Many recent machine learning tasks resort to quan- tum 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…

## 3 Citations

### Poster: Scalable Quantum Convolutional Neural Networks for Edge Computing

- Computer Science2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)
- 2022

A quantum edge computing (QEC) system capable of achieving the scalability of QCNNs, and could be combined to form a large-scale QCNN capable of learning and processing high-dimensional data, overcoming hardware limitations and improving performance.

### Reservoir Computing via Quantum Recurrent Neural Networks

- Computer ScienceArXiv
- 2022

This work approaches sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU, and shows that the quantum version learns faster by requiring fewer training epochs in most cases.

### The state of quantum computing applications in health and medicine

- Medicine
- 2023

Clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective, and quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research.

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