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# Introducing Quantum Machine Learning
Quantum machine learning combines ideas from quantum computing and machine learning. Many researchers are studying how quantum computers could help with machine learning tasks. To support this work, several open-source projects on GitHub share learning resources, examples, and code. These repositories make it easier to understand the basics and see how the field is developing. In this article, we examine five repositories that are especially useful for learning quantum machine learning and understanding the current progress in the space. These resources provide various entry points for different learning styles.
# 1. Mapping the Field
This large list by awesome-quantum-machine-learning (⭐ 3.2k) works like a “table of contents” for the field. It covers basics, algorithms, study materials, and libraries or software. It is excellent for beginners who want to see all the subtopics — such as kernels, variational circuits, or hardware limits — in one place. Licensed under CC0-1.0, it serves as a foundational starting point for anyone wanting to learn the basics of quantum machine learning.
# 2. Exploring Research
The awesome-quantum-ml (⭐ 407) list is smaller and more focused on quality scientific papers and key resources about machine learning algorithms that run on quantum devices. It is ideal if you already know the basics of the field and want a reading queue of papers, surveys, and academic works that explain key concepts, recent findings, and emerging trends in applying quantum computing methods to machine learning problems. The project also accepts contributions from the community via pull requests.
# 3. Learning by Doing
The repository Hands-On-Quantum-Machine-Learning-With-Python-Vol-1 (⭐ 163) contains the code for the book Hands-On Quantum Machine Learning With Python (Vol 1). It is structured like a learning path, allowing you to follow chapters, run experiments, and tweak parameters to see how systems behave. It is perfect for learners who prefer to learn by doing with Python notebooks and scripts.
# 4. Implementing Projects
While it is a smaller repository, Quantum-Machine-Learning-on-Near-Term-Quantum-Devices (⭐ 25) is highly practical. It contains projects that focus on near-term quantum devices — i.e. today’s noisy and limited qubit hardware. The repository includes projects like quantum support vector machines, quantum convolutional neural networks, and data re-uploading models for classification tasks. It highlights real-world constraints, which is useful for observing how quantum machine learning works on current hardware.
# 5. Building Pipelines
This is a full-featured qiskit-machine-learning (⭐ 939) library with quantum kernels, quantum neural networks, classifiers, and regressors. It integrates with PyTorch via the TorchConnector. As part of the Qiskit ecosystem, it is co-maintained by IBM and the Hartree Centre, which is part of the Science and Technology Facilities Council (STFC). It is ideal if you want to build robust quantum machine learning pipelines rather than just study them.
# Developing a Learning Sequence
A productive learning sequence involves starting with one “awesome” list to map the space, using the papers-focused list to build depth, and then alternating between guided notebooks and near-term practical projects. Finally, you can use the Qiskit library as your primary toolkit for experiments that can be extended into full professional workflows.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
