# Classification with Quantum Machine Learning: A Survey

arXiv: https://arxiv.org/abs/2006.12270 · HTML: https://mlsanity.com/paper/2006.12270/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 23 of 23)

- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Quantum Architecture Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.07715/) (2021)
- [Variational Quantum Reinforcement Learning via Evolutionary Optimization](https://mlsanity.com/paper/2109.00540/) (2021)
- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [Quantum machine learning with differential privacy](https://mlsanity.com/paper/2103.06232/) (2021)
- [Quantum Architecture Search via Continual Reinforcement Learning](https://mlsanity.com/paper/2112.05779/) (2021)
- [Hybrid Quantum-Classical Graph Convolutional Network](https://mlsanity.com/paper/2101.06189/) (2021)
- [Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images](https://mlsanity.com/paper/2102.06535/) (2021)
- [Reservoir Computing via Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2211.02612/) (2022)
- [Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications](https://mlsanity.com/paper/2202.11200/) (2022)
- [Scalable Quantum Neural Networks for Classification](https://mlsanity.com/paper/2208.07719/) (2022)
- [SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training](https://mlsanity.com/paper/2301.02601/) (2023)
- [QuFeX: Quantum feature extraction module for hybrid quantum-classical deep neural networks](https://mlsanity.com/paper/2501.13165/) (2025)
- [Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures](https://mlsanity.com/paper/2311.05559/) (2023)
- [MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification](https://mlsanity.com/paper/2307.11875/) (2023)
- [An efficient combination strategy for hybird quantum ensemble classifier](https://mlsanity.com/paper/2210.06785/) (2022)
- [A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models](https://mlsanity.com/paper/2306.03608/) (2023)
- [A Post-Training Approach for Mitigating Overfitting in Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2309.01829/) (2023)
- [Hybrid Quantum Neural Network in High-dimensional Data Classification](https://mlsanity.com/paper/2312.01024/) (2023)
- [Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem](https://mlsanity.com/paper/2405.07226/) (2024)
- [Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2405.16368/) (2024)
- [An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks](https://mlsanity.com/paper/2202.11899/) (2022)
- [Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages](https://mlsanity.com/paper/2602.00128/) (2026)

## Abstract (arXiv)

Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more. Fully towards to combine classical machine learning (ML) with Quantum Information Processing (QIP) to build a new field in the quantum world is called Quantum Machine Learning (QML) to solve and improve problems that displayed in classical machine learning (e.g. time and energy consumption, kernel estimation). The aim of this paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML). Especially, recent QML classification works. Also, we cover about 30 publications that are published lately in Quantum Machine Learning (QML). we propose a classification scheme in the quantum world and discuss encoding methods for mapping classical data to quantum data. Then, we provide quantum subroutines and some methods of Quantum Computing (QC) in improving performance and speed up of classical Machine Learning (ML). And also some of QML applications in various fields, challenges, and future vision will be presented.