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# 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. Several open source projects support this work. GitHub Share learning resources, examples, and code. These repositories 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 advances in the field. These resources provide different entry points for different learning styles.
# 1. Field mapping
This large list includes Amazing quantum machine learning (⭐ 3.2k) acts like a “table of contents” for the field. Covers the basics, algorithms, learning materials, libraries or software. This is great for beginners who want to see all subtopics in one place, such as kernels, variational circuits, and hardware limitations. Licensed under CC0-1.0, it serves as a fundamental starting point for anyone wanting to learn the fundamentals of quantum machine learning.
# 2. Research exploration
of amazing quantum ml (⭐ 407) The list is small and focuses on high-quality scientific papers and important resources about machine learning algorithms running on quantum devices. Ideal if you already understand the basics of the field and want to read a large number of papers, studies, and academic works that explain key concepts, recent discoveries, and emerging trends in applying quantum computing techniques to machine learning problems. This project also accepts contributions from the community through pull requests.
# 3. Learn by doing
repository Practical learning of quantum machines using Python Vol-1 (⭐ 163) Contains code for this book Practical Quantum Machine Learning with Python (Vol 1). It is structured like a learning path, allowing you to follow the chapters and run experiments, adjusting parameters to see how the system behaves. Perfect for learners who like to learn by doing. python notes and scripts.
# 4. Project implementation
Although it is a small repository, Quantum machine learning on short-term quantum devices (⭐ 25) is very practical. This includes projects focused on near-term quantum devices, or today’s noisy and limited qubit hardware. This repository includes projects such as quantum support vector machines, quantum convolutional neural networks, and data reupload models for classification tasks. This highlights real-world constraints and is useful for observing how quantum machine learning works on current hardware.
# 5. Building the pipeline
this is full featured qiskit-machine learning (⭐ 939) A library with quantum kernels, quantum neural networks, classifiers, and regressors. integrate with pie torch through TorchConnector. As part of the kiss kit The ecosystem is jointly maintained by: IBM and hartley centerpart of the Science and Technology Facilities Council (STFC). Ideal if you want to build robust quantum machine learning pipelines rather than just research.
# Develop a learning sequence
A productive learning sequence includes starting with one “great” list for mapping a space, building depth with paper-focused lists, and then alternating between guided notebooks and short-term practice projects. Finally, you can use the Qiskit library as your primary toolkit for experimentation and extend it into a complete professional workflow.
kanwar mereen I’m a machine learning engineer and technical writer with a deep passion for the intersection of data science, AI, and healthcare. She co-authored the e-book “Maximize Productivity with ChatGPT.” She champions diversity and academic excellence as a 2022 Google Generation Scholar for APAC. She has also been recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change and founded FEMCodes to empower women in STEM fields.
