# Systematic Literature Review: Quantum Machine Learning and its applications

arXiv: https://arxiv.org/abs/2201.04093 · HTML: https://mlsanity.com/paper/2201.04093/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 138 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 27 of 27)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [Orthogonal Convolutional Neural Networks](https://mlsanity.com/paper/1911.12207/) (2019)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Robust data encodings for quantum classifiers](https://mlsanity.com/paper/2003.01695/) (2020)
- [Layerwise learning for quantum neural networks](https://mlsanity.com/paper/2006.14904/) (2020)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Recurrent Quantum Neural Networks](https://mlsanity.com/paper/2006.14619/) (2020)
- [Orthogonal Deep Neural Networks](https://mlsanity.com/paper/1905.05929/) (2019)
- [TensorFlow Quantum: A Software Framework for Quantum Machine Learning](https://mlsanity.com/paper/2003.02989/) (2020)
- [Foundations for Near-Term Quantum Natural Language Processing](https://mlsanity.com/paper/2012.03755/) (2020)
- [Quantum Language Model with Entanglement Embedding for Question Answering](https://mlsanity.com/paper/2008.09943/) (2020)
- [Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions](https://mlsanity.com/paper/2006.01976/) (2020)
- [Coreset Clustering on Small Quantum Computers](https://mlsanity.com/paper/2004.14970/) (2020)
- [A Practical Quantum Instruction Set Architecture](https://mlsanity.com/paper/1608.03355/) (2016)
- [Detecting quantum entanglement with unsupervised learning](https://mlsanity.com/paper/2103.04804/) (2021)
- [Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2009.06767/) (2020)
- [Trainable Discrete Feature Embeddings for Variational Quantum Classifier](https://mlsanity.com/paper/2106.09415/) (2021)
- [Solving machine learning optimization problems using quantum computers](https://mlsanity.com/paper/1911.08587/) (2019)
- [Quantum Neural Machine Learning - Backpropagation and Dynamics](https://mlsanity.com/paper/1609.06935/) (2016)
- [Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices](https://mlsanity.com/paper/1801.02363/) (2018)
- [Classifying Data with Local Hamiltonians](https://mlsanity.com/paper/1807.00804/) (2018)

## Built on by (most cited first, 30 of 32)

- [Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code](https://mlsanity.com/paper/2405.19495/) (2024)
- [Rapid training of quantum recurrent neural networks](https://mlsanity.com/paper/2207.00378/) (2022)
- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [A Primer on Quantum Machine Learning](https://mlsanity.com/paper/2511.15969/) (2025)
- [Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection](https://mlsanity.com/paper/2412.19441/) (2024)
- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus](https://mlsanity.com/paper/2510.06228/) (2025)
- [Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations](https://mlsanity.com/paper/2412.08010/) (2024)
- [Review of Ansatz Designing Techniques for Variational Quantum Algorithms](https://mlsanity.com/paper/2212.04913/) (2022)
- [Jet Discrimination with Quantum Complete Graph Neural Network](https://mlsanity.com/paper/2403.04990/) (2024)
- [Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU](https://mlsanity.com/paper/2404.06059/) (2024)
- [Modeling Quantum Machine Learning for Genomic Data Analysis](https://mlsanity.com/paper/2501.08193/) (2025)
- [Expressibility-Enhancing Strategies for Quantum Neural Networks](https://mlsanity.com/paper/2211.12670/) (2022)
- [Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models](https://mlsanity.com/paper/2308.11112/) (2023)
- [Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2405.16368/) (2024)
- [Machine learning approach to brain tumor detection and classification](https://mlsanity.com/paper/2410.12692/) (2024)
- [Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study](https://mlsanity.com/paper/2501.04690/) (2025)
- [Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting](https://mlsanity.com/paper/2504.00068/) (2025)
- [Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning](https://mlsanity.com/paper/2508.00024/) (2025)
- [Multi-channel convolutional neural quantum embedding](https://mlsanity.com/paper/2509.22355/) (2025)
- [Quantum Machine Learning for Image Classification: A Hybrid Model of Residual Network with Quantum Support Vector Machine](https://mlsanity.com/paper/2510.23659/) (2025)
- [Quantum Implicit Neural Representations for Novel View Synthesis](https://mlsanity.com/paper/2601.05250/) (2026)
- [Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning](https://mlsanity.com/paper/2602.00048/) (2026)
- [Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models](https://mlsanity.com/paper/2605.02798/) (2026)
- [Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study](https://mlsanity.com/paper/2605.27923/) (2026)
- [Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines](https://mlsanity.com/paper/2310.10910/) (2023)
- [Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications](https://mlsanity.com/paper/2503.07681/) (2025)
- [Single-Qudit Quantum Neural Networks for Multiclass Classification](https://mlsanity.com/paper/2503.09269/) (2025)
- [Quantum Scrambling Born Machine](https://mlsanity.com/paper/2602.17281/) (2026)
- [Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings](https://mlsanity.com/paper/2604.24597/) (2026)

## Abstract (arXiv)

Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles for subsequent use in performing calculations, as well as for large-scale information processing. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, there are scientific challenges that are impossible to perform by classical computation due to computational complexity or the time the calculation would take, and quantum computation is one of the possible answers. However, current quantum devices have not yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023 to identify, analyze and classify the different algorithms used in quantum machine learning and their applications. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. Many articles try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in the quantum hardware is required since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.