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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 can help with machine learning tasks. To support this work, several open source projects have been opened GitHub sharing learning resources, examples, and code. These endpoints make it easy to understand the basics and see how the field is developing. In this article, we examine five databases that are particularly useful for studying quantum mechanics and understanding current progress in the space. These resources provide various entry points for different learning styles.
# 1. Mapping the Field
This great list is learning-a-quantum-machine-amazing-machine (⭐ 3.2k) serves as a “table of contents” for the field. It includes foundations, algorithms, learning materials, and libraries or software. It's great for beginners who want to see all the sub-topics – like characters, various circuits, or hardware limitations – in one place. Licensed under CC0-1.0, it serves as a primer for anyone who wants to learn the basics of quantum machine learning.
# 2. Research Evaluation
I amazing-quantum-ml (⭐ 407) list is small and focused on quality scientific papers and valuable resources about machine learning algorithms working on quantum devices. It's great if you already know the basics of the field and want to read a line of papers, surveys, and academic works that describe key concepts, recent findings, and emerging trends in applying quantum computing methods to machine learning problems. The project also accepts donations from the community through pull requests.
# 3. Learning by Doing
A repository Hands-on-Quantum-Machine-Learning-With-Python-Vol-1 (⭐ 163) contains the book code Hands On Quantum Machine Learning With Python (Vol 1). Organized as a learning method, it allows you to follow the chapters, run experiments, and adjust structures to see how the systems behave. Ideal for students who prefer to learn by doing Python notebooks and documents.
# 4. Commencement of Works
Although it is a small cache, Quantum-Machine-Learning-on-Near-Term-Quantum-Devices (⭐ 25) is very effective. It contains projects focused on near-term quantum devices – i.e. today's hardware that is noisy and limited. The repository includes projects such as quantum support machines, quantum convolutional neural networks, and data reloading models for classification functions. It highlights real-world issues, which are useful for looking at how quantum machine learning works on current hardware.
# 5. Construction Pipes
This is a full feature qiskit-machine-learning (⭐ 939) library with quantum kernels, quantum neural networks, classifiers, and regressors. It includes PyTorch with TorchConnector. As part of Qiskit ecosystem, is maintained by IBM as well as Hartree Centrewhich is part of the Science and Technology Resources Council (STFC). It's great if you want to build robust quantum machine learning pipelines rather than just learning them.
# Developing Learning Sequences
A productive learning sequence involves starting with a single “wonder” list to map the area, using a focused list of papers to build depth, and then alternating with guided manuals and near-term practical projects. Finally, you can use the Qiskit library as your primary toolkit for testing that can be expanded into a complete workflow.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI and medicine. He co-authored the ebook “Increasing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, he strives for diversity and academic excellence. He has also been 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.
