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Top Quantum Machine Learning Projects for Learning

Ten hands-on quantum machine learning projects — each paired with a public dataset and a named quantum algorithm — plus tutorials by technique, datasets and key papers to take you from QML theory to working code.

38 topics•5 categories•Updated October 2026

The 10 Projects

10 entries

Each project pairs a public dataset with a named quantum algorithm. The link on each card goes to the dataset.

011. Quantum Image ClassificationClassify handwritten digits by encoding pixel features into qubits and training a variational circuit. Algorithm: Variational Quantum Classifier (VQC). Dataset: MNIST. 022. Quantum Credit Card Fraud DetectionFlag fraudulent transactions in a highly imbalanced dataset using a quantum-kernel support vector machine. Algorithm: QSVM. Dataset: Credit Card Fraud (Kaggle). 033. Quantum Breast Cancer PredictionPredict malignant versus benign tumours from cell-nuclei features using a quantum neural network classifier. Algorithm: QNN. Dataset: Breast Cancer Wisconsin (UCI). 044. Quantum Sentiment AnalysisClassify text polarity by combining classical embeddings with a quantum circuit layer. Algorithm: Hybrid Quantum NLP. Dataset: Sentiment140. 055. Quantum Stock Price PredictionForecast price sequences by feeding historical market data through a quantum LSTM with variational circuits. Algorithm: Quantum LSTM. Dataset: Yahoo Finance. 066. Quantum Drug DiscoveryPredict molecular properties for candidate compounds using quantum kernel methods over chemical feature spaces. Algorithm: Quantum Kernel Methods. Dataset: MoleculeNet (via DeepChem). 077. Quantum Customer Churn PredictionPredict which telecom customers are likely to churn using a quantum support vector machine. Algorithm: QSVM. Dataset: Telco Customer Churn (Kaggle). 088. Quantum Intrusion DetectionDetect anomalous network traffic by compressing flow features with a quantum autoencoder. Algorithm: Quantum Autoencoder. Dataset: NSL-KDD. 099. Quantum Recommendation SystemRecommend items by factorising the user-item ratings matrix with a quantum approach. Algorithm: Quantum Matrix Factorisation. Dataset: MovieLens. 1010. Quantum Medical Image ClassificationClassify biomedical images with a quantum convolutional neural network. Algorithm: QCNN. Dataset: MedMNIST.

Hands-on Tutorials by Technique

16 entries

Worked examples you can adapt for the projects above.

11Variational Classifier (PennyLane)Build a quantum classifier with a variational circuit; the basis for projects 1 and 3. 12Training and Evaluating Quantum Kernels (PennyLane)Quantum kernels for SVM-style classifiers; basis for projects 2, 6 and 7. 13Quantum Kernel Tutorial (Qiskit Machine Learning)Use a quantum kernel with a classical SVM in Qiskit. 14Neural Networks Tutorial (Qiskit Machine Learning)Quantum neural networks in Qiskit; basis for project 3. 15Training a Quantum Model on a Real Dataset (Qiskit)End-to-end classification on a real dataset. 16Hybrid Networks with PyTorch and QiskitCombine a quantum layer with a PyTorch model; useful for project 4. 17Quantum Autoencoder (Qiskit Machine Learning)Compress data with a quantum autoencoder; basis for project 8. 18Quantum Convolutional Neural Networks (Qiskit)QCNN in Qiskit; basis for project 10. 19QCNN Tutorial (TensorFlow Quantum)Quantum convolutional neural network in TensorFlow Quantum. 20MNIST Classification (TensorFlow Quantum)Classify MNIST digits with a quantum model; basis for project 1. 21Quanvolutional Neural Networks (PennyLane)Quantum convolution-like filters for image data. 22Quantum Transfer Learning (PennyLane)Attach a quantum classifier to a pretrained network. 23Data Re-uploading Classifier (PennyLane)Encode data repeatedly to increase model expressivity. 24Quantum Graph Recurrent Neural Network (PennyLane)Sequence-style quantum model, relevant to project 5. 25IBM Quantum Learning – Quantum Machine LearningIBM's course on quantum machine learning. 26PennyLane DemonstrationsFull gallery of PennyLane quantum machine learning demos.

Datasets & Classical Baselines

5 entries

Where to get data and compare against classical models.

27scikit-learn Toy DatasetsSmall built-in datasets ideal for quantum experiments. 28Breast Cancer Dataset (scikit-learn)Loader for the Breast Cancer Wisconsin dataset. 29OpenMLOpen repository of machine learning datasets and benchmarks. 30MovieLens (TensorFlow Datasets)Loader for the MovieLens datasets. 31Sentiment140 (Hugging Face)Hugging Face copy of the Sentiment140 dataset.

Key Papers

3 entries

Papers behind the main algorithms used in these projects.

32Supervised Learning with Quantum-Enhanced Feature SpacesQuantum kernel and variational classifiers demonstrated on hardware. 33Quantum Convolutional Neural NetworksThe QCNN architecture used in project 10. 34Quanvolutional Neural NetworksQuantum convolution-like filters for image data.

Related Pages

4 entries

More quantum machine learning pages on this site.

35Quantum Machine LearningQML explainers, frameworks and papers. 36Quantum AIQuantum AI hub with variational circuits, QAOA and VQE. 37Quantum Computing LanguagesLanguages and SDKs for programming quantum computers. 38Quantum ComputingHub for quantum computing topics on this site.

Frequently Asked Questions

Which framework should I use for these projects?

PennyLane and Qiskit Machine Learning have the most tutorials and work with PyTorch or scikit-learn. TensorFlow Quantum suits Keras workflows, and all can run on simulators for free.

Do I need a real quantum computer?

No. Start on simulators with small datasets, using a few qubits and reduced features, and move to cloud hardware such as IBM Quantum or Amazon Braket when ready.

Will the quantum model beat a classical one?

Usually not on these datasets today. Treat each project as a way to learn the algorithms and compare against a classical baseline such as logistic regression or an SVM.

How do I fit large datasets into a few qubits?

Reduce features first, for example with PCA, then encode them with angle or amplitude encoding, and use small subsets of the data for training.

Why do some dataset links go to Kaggle?

Credit Card Fraud and Telco Churn are most commonly obtained from Kaggle, which needs a free login. Other data are available from UCI, OpenML, TensorFlow Datasets or Hugging Face.