# A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification

arXiv: https://arxiv.org/abs/2201.01820 · HTML: https://mlsanity.com/paper/2201.01820/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Continuous-variable quantum neural networks](https://mlsanity.com/paper/1806.06871/) (2018)
- [An end-to-end trainable hybrid classical-quantum classifier](https://mlsanity.com/paper/2102.02416/) (2021)
- [Representation Learning via Quantum Neural Tangent Kernels](https://mlsanity.com/paper/2111.04225/) (2021)
- [Hybrid quantum-classical classifier based on tensor network and variational quantum circuit](https://mlsanity.com/paper/2011.14651/) (2020)
- [Advances in Quantum Deep Learning: An Overview](https://mlsanity.com/paper/2005.04316/) (2020)
- [Adiabatic Quantum Linear Regression](https://mlsanity.com/paper/2008.02355/) (2020)
- [Simulating a perceptron on a quantum computer](https://mlsanity.com/paper/1412.3635/) (2014)
- [Balanced k-Means Clustering on an Adiabatic Quantum Computer](https://mlsanity.com/paper/2008.04419/) (2020)
- [Quantum Discriminator for Binary Classification](https://mlsanity.com/paper/2009.01235/) (2020)

## Built on by (most cited first, 11 of 11)

- [Towards Quantum Federated Learning](https://mlsanity.com/paper/2306.09912/) (2023)
- [Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation](https://mlsanity.com/paper/2306.11297/) (2023)
- [Variational Quantum Neural Networks (VQNNS) in Image Classification](https://mlsanity.com/paper/2303.05860/) (2023)
- [Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization](https://mlsanity.com/paper/2201.08629/) (2022)
- [Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems](https://mlsanity.com/paper/2304.10159/) (2023)
- [Towards Quantum Machine Learning for Malicious Code Analysis](https://mlsanity.com/paper/2508.19381/) (2025)
- [On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks](https://mlsanity.com/paper/2302.10467/) (2023)
- [Computing the gradients with respect to all parameters of a quantum neural network using a single circuit](https://mlsanity.com/paper/2307.08167/) (2023)
- [Quantum neural networks facilitating quantum state classification](https://mlsanity.com/paper/2504.06622/) (2025)
- [Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines](https://mlsanity.com/paper/2504.20823/) (2025)
- [Quantum anomaly detection in real scarce data](https://mlsanity.com/paper/2610.09635/) (2026)

## Abstract (arXiv)

Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become increasingly pressing as conventional computers quickly approach physical limitations that will slow performance improvements in years to come. For these reasons, scientists have begun to explore alternative computing platforms, like quantum computers, for training neural networks. In recent years, variational quantum circuits have emerged as one of the most successful approaches to quantum deep learning on noisy intermediate scale quantum devices. We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit. We empirically analyze the performance of this hybrid neural network on a series of binary classification data sets using a simulated universal quantum computer and a state of the art universal quantum computer. On simulated hardware, we observe that the hybrid neural network achieves roughly 10% higher classification accuracy and 20% better minimization of cost than an individual variational quantum circuit. On quantum hardware, we observe that each model only performs well when the qubit and gate count is sufficiently small.