# Scalable Quantum Neural Networks for Classification

arXiv: https://arxiv.org/abs/2208.07719 · HTML: https://mlsanity.com/paper/2208.07719/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Continuous-variable quantum neural networks](https://mlsanity.com/paper/1806.06871/) (2018)
- [TensorFlow Quantum: A Software Framework for Quantum Machine Learning](https://mlsanity.com/paper/2003.02989/) (2020)
- [Stochastic gradient descent for hybrid quantum-classical optimization](https://mlsanity.com/paper/1910.01155/) (2019)
- [Quantum federated learning through blind quantum computing](https://mlsanity.com/paper/2103.08403/) (2021)
- [Support vector machines on the D-Wave quantum annealer](https://mlsanity.com/paper/1906.06283/) (2019)
- [Entangled Datasets for Quantum Machine Learning](https://mlsanity.com/paper/2109.03400/) (2021)
- [Hybrid quantum-classical classifier based on tensor network and variational quantum circuit](https://mlsanity.com/paper/2011.14651/) (2020)
- [Classification with Quantum Machine Learning: A Survey](https://mlsanity.com/paper/2006.12270/) (2020)
- [QuantumFed: A Federated Learning Framework for Collaborative Quantum Training](https://mlsanity.com/paper/2106.09109/) (2021)
- [Quantum-Inspired Support Vector Machine](https://mlsanity.com/paper/1906.08902/) (2019)
- [Quantum perceptron over a field and neural network architecture selection in a quantum computer](https://mlsanity.com/paper/1602.00709/) (2016)
- [LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks](https://mlsanity.com/paper/2205.02666/) (2022)

## Built on by (most cited first, 6 of 6)

- [Reservoir Computing via Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2211.02612/) (2022)
- [QTrojan: A Circuit Backdoor Against Quantum Neural Networks](https://mlsanity.com/paper/2302.08090/) (2023)
- [Distributed Quantum Neural Networks via Partitioned Features Encoding](https://mlsanity.com/paper/2312.13650/) (2023)
- [MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification](https://mlsanity.com/paper/2307.11875/) (2023)
- [A Scalable Quantum Non-local Neural Network for Image Classification](https://mlsanity.com/paper/2407.18906/) (2024)
- [A Quantum Variational Approach to Prototypical Recurrent Unit](https://mlsanity.com/paper/2609.04354/) (2026)

## Abstract (arXiv)

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.