mlsanityData to 7 Oct 2026

Systematic Literature Review: Quantum Machine Learning and its applications

arXiv 2201.04093 ↗First posted 11 Jan 2022quant-ph cs.LGcited by 32

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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.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 27 of its 138 references that are in the map. First posted on arXiv 11 Jan 2022.

Builds on

27 of 138 references.

References · 27

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  2. 2018Supervised learning with quantum enhanced feature spacescited by 654
  3. 2020Power of data in quantum machine learningcited by 336
  4. 2019Parameterized quantum circuits as machine learning modelscited by 330
  5. 2016Quantum Machine Learningcited by 325
  6. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  7. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  8. 2019Orthogonal Convolutional Neural Networkscited by 122
  9. 2019Transfer learning in hybrid classical-quantum neural networkscited by 113
  10. 2020Robust data encodings for quantum classifierscited by 91
  11. 2020Layerwise learning for quantum neural networkscited by 87
  12. 2021Federated Quantum Machine Learningcited by 82
15 more
  1. 2020Recurrent Quantum Neural Networkscited by 79
  2. 2019Orthogonal Deep Neural Networkscited by 72
  3. 2020TensorFlow Quantum: A Software Framework for Quantum Machine Learningcited by 72
  4. 2020Foundations for Near-Term Quantum Natural Language Processingcited by 51
  5. 2020Quantum Language Model with Entanglement Embedding for Question Answeringcited by 17
  6. 2020Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributionscited by 11
  7. 2020Coreset Clustering on Small Quantum Computerscited by 10
  8. 2016A Practical Quantum Instruction Set Architecturecited by 9
  9. 2021Detecting quantum entanglement with unsupervised learningcited by 9
  10. 2020Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentationcited by 7
  11. 2021Trainable Discrete Feature Embeddings for Variational Quantum Classifiercited by 4
  12. 2019Solving machine learning optimization problems using quantum computerscited by 3
  13. 2016Quantum Neural Machine Learning - Backpropagation and Dynamicscited by 2
  14. 2018Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devicescited by 2
  15. 2018Classifying Data with Local Hamiltonianscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 8; 2025: 10; 2026: 8.

32 papers.

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  1. 2024Qiskit Code Assistant: Training LLMs for generating Quantum Computing Codecited by 24
  2. 2022Rapid training of quantum recurrent neural networkscited by 13
  3. 2023Quantum-Inspired Machine Learning: a Surveycited by 11
  4. 2024Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detectioncited by 10
  5. 2024Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspectivecited by 9
  6. 2025A Primer on Quantum Machine Learningcited by 9
  7. 2024Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulationscited by 4
  8. 2025Layerwise Federated Learning for Heterogeneous Quantum Clients using Quoruscited by 4
  9. 2022Review of Ansatz Designing Techniques for Variational Quantum Algorithmscited by 3
  10. 2024Jet Discrimination with Quantum Complete Graph Neural Networkcited by 3
  11. 2024Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLUcited by 2
  12. 2025Modeling Quantum Machine Learning for Genomic Data Analysiscited by 2
20 more
  1. 2022Expressibility-Enhancing Strategies for Quantum Neural Networkscited by 1
  2. 2023Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Modelscited by 1
  3. 2024Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detectioncited by 1
  4. 2024Machine learning approach to brain tumor detection and classificationcited by 1
  5. 2025Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Studycited by 1
  6. 2025Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecastingcited by 1
  7. 2025Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learningcited by 1
  8. 2025Multi-channel convolutional neural quantum embeddingcited by 1
  9. 2025Quantum Machine Learning for Image Classification: A Hybrid Model of Residual Network with Quantum Support Vector Machinecited by 1
  10. 2026Quantum Implicit Neural Representations for Novel View Synthesiscited by 1
  11. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
  12. 2026Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Modelscited by 1
  13. 2026Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Studycited by 1
  14. 2023Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machinescited by 0
  15. 2025Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applicationscited by 0
  16. 2025Single-Qudit Quantum Neural Networks for Multiclass Classificationcited by 0
  17. 2026Quantum Scrambling Born Machinecited by 0
  18. 2026Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddingscited by 0
  19. 2026Systematic Literature Review of Machine Learning Models and Applications for Text Recognitioncited by 0
  20. 2026Evaluating Hybrid Quantum-Classical Models for Reduced-Order Brain Deformation Dynamicscited by 0