# On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

arXiv: https://arxiv.org/abs/2109.09484 · HTML: https://mlsanity.com/paper/2109.09484/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 82 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Quantum Variational Autoencoder](https://mlsanity.com/paper/1802.05779/) (2018)
- [Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study](https://mlsanity.com/paper/2010.07830/) (2020)
- [Towards a Collective Agenda on AI for Earth Science Data Analysis](https://mlsanity.com/paper/2104.05107/) (2021)
- [Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks](https://mlsanity.com/paper/1609.06846/) (2016)
- [A hybrid quantum-classical neural network with deep residual learning](https://mlsanity.com/paper/2012.07772/) (2020)
- [Deep Discriminative Representation Learning with Attention Map for Scene Classification](https://mlsanity.com/paper/1902.07967/) (2019)
- [Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing](https://mlsanity.com/paper/2101.10657/) (2021)
- [Image Classification via Quantum Machine Learning](https://mlsanity.com/paper/2011.02831/) (2020)
- [Land Use and Land Cover Classification using a Human Group based Particle Swarm Optimization Algorithm with a LSTM classifier on hybrid-pre-processing Remote Sensing Images](https://mlsanity.com/paper/2008.01635/) (2020)
- [IGO-QNN: Quantum Neural Network Architecture for Inductive Grover Oracularization](https://mlsanity.com/paper/2105.11603/) (2021)

## Built on by (most cited first, 30 of 33)

- [Quantum Convolutional Neural Networks are Effectively Classically Simulable](https://mlsanity.com/paper/2408.12739/) (2024)
- [Hybrid quantum ResNet for car classification and its hyperparameter optimization](https://mlsanity.com/paper/2205.04878/) (2022)
- [Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines](https://mlsanity.com/paper/2302.08270/) (2023)
- [Rapid training of quantum recurrent neural networks](https://mlsanity.com/paper/2207.00378/) (2022)
- [Forecasting steam mass flow in power plants using the parallel hybrid network](https://mlsanity.com/paper/2307.09483/) (2023)
- [Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images](https://mlsanity.com/paper/2310.02323/) (2023)
- [From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks](https://mlsanity.com/paper/2408.06524/) (2024)
- [A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification](https://mlsanity.com/paper/2303.11705/) (2023)
- [3D-QAE: Fully Quantum Auto-Encoding of 3D Point Clouds](https://mlsanity.com/paper/2311.05604/) (2023)
- [Variational Quantum Neural Networks (VQNNS) in Image Classification](https://mlsanity.com/paper/2303.05860/) (2023)
- [Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection](https://mlsanity.com/paper/2311.06222/) (2023)
- [HQF-Net: A Hybrid Quantum-Classical Multi-Scale Fusion Network for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2604.06715/) (2026)
- [Beyond Ans"atze: Learning Quantum Circuits as Unitary Operators](https://mlsanity.com/paper/2203.00601/) (2022)
- [Satellite image classification with neural quantum kernels](https://mlsanity.com/paper/2409.20356/) (2024)
- [A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor](https://mlsanity.com/paper/2412.16867/) (2024)
- [Quantum autoencoders for image classification](https://mlsanity.com/paper/2502.15254/) (2025)
- [Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networks](https://mlsanity.com/paper/2504.12389/) (2025)
- [QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification](https://mlsanity.com/paper/2604.11817/) (2026)
- [Quantum Machine Learning in Climate Change and Sustainability: a Review](https://mlsanity.com/paper/2310.09162/) (2023)
- [On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation](https://mlsanity.com/paper/2410.08677/) (2024)
- [Explainable Quantum Machine Learning for Multispectral Images Segmentation: Case Study](https://mlsanity.com/paper/2503.08962/) (2025)
- [Quantum Generative Diffusion Model for Real-World Time Series](https://mlsanity.com/paper/2606.27561/) (2026)
- [Quantum Machine Learning for Remote Sensing: Exploring potential and challenges](https://mlsanity.com/paper/2311.07626/) (2023)
- [Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal](https://mlsanity.com/paper/2402.06684/) (2024)
- [Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio](https://mlsanity.com/paper/2403.02080/) (2024)
- [Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks](https://mlsanity.com/paper/2410.05777/) (2024)
- [Leveraging Multi-Temporal Sentinel 1 and 2 Satellite Data for Leaf Area Index Estimation With Deep Learning](https://mlsanity.com/paper/2410.19787/) (2024)
- [Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach](https://mlsanity.com/paper/2601.07882/) (2026)
- [Auto Quantum Machine Learning for Multisource Classification](https://mlsanity.com/paper/2602.18642/) (2026)
- [Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification](https://mlsanity.com/paper/2604.26675/) (2026)

## Abstract (arXiv)

This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case, and tested on the EuroSAT dataset used as reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for futures investigations.