A canonical compendium of quantum machine learning code in the year before fault tolerance. Framework-agnostic, organized as both reference and recovery atlas — preserving the early code of QML (Wittek’s MOOC, Rigetti’s Grove, Zapata, Microsoft LIQUi|⟩, qiskit-aqua) before it vanishes. PDF mirror included.

Principles, Techniques, and Future Directions Prompt engineering, at its core, represents the art and science of meticulously designing and refining textual inputs, known as prompts, to effectively guide artificial intelligence models, particularly large language models (LLMs) and generative AI, towards producing desired and relevant outputs. This burgeoning field focuses on crafting effective prompts that unlock the capabilities of LLMs, enabling them to understand intent, follow instructions, and generate meaningful responses across a wide array of […]

Riding the Wave The software development landscape is undergoing a profound transformation, with artificial intelligence (AI) emerging as a central force shaping how software is conceived and brought to life. Among the novel trends capturing the attention of the technology community is “vibe coding,” a programming paradigm that gained significant traction in early 2025. This approach signifies a fundamental shift away from traditional manual coding practices, with AI taking on a much more active role […]