Photo by author
# Introduction to Quantum Machine Learning
Quantum machine learning combines ideas from quantum computing and machine learning. Many researchers are studying how quantum computers can help with machine learning tasks. To support this work, several open source projects GitHub Share learning resources, examples and code. These collections make it easy to understand the basics and see how the field is developing. In this article, we review five repositories that are particularly useful for learning quantum machine learning and understanding current developments in the space. These resources provide different entry points for different learning styles.
# 1. Mapping the field
This huge list by Very good quantum machine learning (⭐ 3.2k) acts like a “table of contents” for the field. It includes fundamentals, algorithms, study materials, and libraries or software. It’s perfect for beginners who want to see all the subtopics — like kernels, variable circuits, or hardware limitations — in one place. Licensed under CC0-1.0, it serves as a basic starting point for anyone wanting to learn the basics of quantum machine learning.
# 2. Research Findings
gave awesome-quantum-ml (⭐ 407) List focuses on quality scientific papers and key resources about machine learning algorithms running on miniature and quantum devices. It is ideal if you already know the basics of the field and want to read a range of papers, surveys, and academic works that explain key concepts, recent results, 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
Repository Hands-On-Quantum-Machine-Learning-With-Python-Vol-1 (⭐ 163) contains the book code. Hands-on Quantum Machine Learning with Python (Volume 1). It’s 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 learning. The python Notebook and script.
# 4. Implementing plans
While this is a small collection, Quantum-machine-learning-on-near-term-quantum-devices (⭐ 25) is highly practical. This includes projects that focus on near-quantum devices—that is, today’s noisy and limited qubit hardware. The repository includes projects such as 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 seeing how quantum machine learning works on existing hardware.
# 5. Building pipelines
It is a full featured one. qiskit-machine-learning (⭐ 939) Library containing quantum kernels, quantum neural networks, classifiers, and regressors. merges with Pi flashlight through TorchConnector. As part of Casket Ecosystems, it sustains in common IBM And Hartree Centrewhich is part of the Science and Technology Facilities Council (STFC). This is ideal if you want to build robust quantum machine learning pipelines rather than studying them.
# Developing a learning environment
A productive learning sequence involves starting with a “scary” list to map the space, using a focused list of papers to build depth, and then alternating between guided notebooks and near-term action plans. Finally, you can use the Qiskit library as your core toolkit for experiments that can be expanded into full professional workflows.
Kanwal Mehreen is a machine learning engineer and a technical writer with a deep passion for AI along with data science and medicine. He co-authored the e-book “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she is a champion of diversity and academic excellence. She is also recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, having founded FEMCodes to empower women in STEM fields.