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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. To support this work, several open-source projects are ongoing. 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 examine five repositories that are particularly useful for learning quantum machine learning and understanding current progress in the space. These resources provide different entry points for different learning styles.
# 1. Mapping the field
This big list by amazing-quantum-machine-learning (⭐ 3.2k) Serves as a “table of contents” for the region. It includes basics, algorithms, study material and libraries or software. It is excellent for beginners who want to see all subtopics – such as kernels, variational circuits, or hardware limitations – in one place. Licensed under CC0-1.0, it serves as a fundamental starting point for anyone wanting to learn the basics of quantum machine learning.
# 2. Research Discovery
amazing-quantum-ml (⭐ 407) The list is shorter and more focused on quality scientific papers and key resources about machine learning algorithms running 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 through pull requests.
# 3. Learning by doing
storehouse Hands-on-Quantum-Machine-Learning-with-Python-Volume-1 (⭐ 163) contains the code for the book Hands-on Quantum Machine Learning with Python (Volume 1). It is structured like a learning path, allowing you to follow chapters, run experiments, and change parameters to see how the systems behave. It’s perfect for learners who like to learn by doing Python Notebook and script.
# 4. Implementation of projects
Although it is a small store, quantum-machine-learning-on-near-term-quantum-devices (⭐ 25) is highly practical. This includes projects that focus on near-term quantum devices – i.e. 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. This highlights real-world constraints, which is useful for seeing how quantum machine learning works on current hardware.
# 5. Construction of pipelines
It is full featured quickkit-machine-learning (⭐ 939) Library with quantum kernels, quantum neural networks, classifiers and regressors. It integrates with pytorch Through TorchConnector. as part of biscuits Ecosystem, it is co-maintained by IBM and this Hartree CenterWhich is part of the Science and Technology Facilitation Council (STFC). This is ideal if you want to build robust quantum machine learning pipelines rather than just study them.
# develop learning sequence
A productive learning sequence involves starting with a “wonderful” list to map out 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 Kiskit library as your primary toolkit for experiments that can be scaled into a full professional workflow.
Kanwal Mehreen He is a machine learning engineer and a technical writer with a deep passion for the intersection of AI with data science and medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she is an advocate for diversity and academic excellence. She has also been recognized as a Teradata Diversity in Tech Scholar, a Mitex GlobalLink Research Scholar, and a Harvard VCode Scholar. Kanwal is a strong advocate for change, having founded FEMCodes to empower women in STEM fields.
