awesome-quantum-machine-learning

Why we included this project

This curated list is a solid reading path into quantum machine learning, useful if you want to understand the field before picking a framework or library. It starts with the physics basics, covering qubits, superposition, entanglement, and quantum gates, then moves through the concepts that connect quantum computing to classical machine learning, and finally into concrete algorithms and their classical counterparts. The sections gather quantum versions of familiar techniques like k-nearest neighbors, k-means, support vector machines, and neural networks, plus study materials and software links grouped by language. For a developer or researcher getting started, it works as a structured syllabus that maps the territory and points to the papers and tools worth reading next, rather than as a deployable codebase.