Quantum as a service en machine learning: una guía educativa de adopción y aplicación frente a la nube tradicional

Abstract

The project aimed to develop a teaching guide as a means to facilitate the progressive learning of quantum computing through Quantum as a Service (QaaS), applying its power in the training of machine learning models, one of the branches that has advanced the most in technological development in recent times. The proposal combines a progressive pedagogical approach with an accessible graphic model, seeking to guide the reader from the basic concepts of quantum computing to its practical application through real exercises. A five-phase methodology was used for its development: conceptual exploration of quantum computing, development of practical exercises using the power of QaaS, design of the teaching guide based on the two previous points and a progressive learning system, followed by validation with the support of the university community to obtain real feedback on how this type of resource is perceived and its use in real environments. The feedback obtained during the validation process allowed us to strengthen the structure of the guide, improving the clarity of the content and reinforcing the points where limitations or difficulties in understanding it were evident. Based on these contributions, a final version was consolidated in line with the purpose of the project: to offer a clear, accessible, and relevant learning resource to address the challenges of understanding quantum computing in the cloud. Translated with DeepL.com (free version)