دانلود کتاب مقدمه‌ای مختصر بر یادگیری ماشین کوانتومی

Quantum computers, as next-generation computational devices, harness the quan-tum principles of superposition and entanglement to process information in ways fundamentally different from classical computers. These unique properties enable quantum computers to address many practical problems that are intractable for classical computers. Although quantum computing is still in its early stages, we have entered an era since 2019 where quantum supremacy has been experimentally demonstrated by several research groups and industrial organizations, underscoring the immense potential of quantum technologies to transform various aspects of everyday life.
Machine learning (ML) is widely regarded as one of the most promising and impactful applications of quantum computing. The ability of quantum computing to accelerate advancements in foundational models, such as generative pre-trained transformers (GPTs), and even pave the way toward artificial general intelligence (AGI), is particularly compelling. Recent progress in both theories and experiments has exhibited the power of quantum machine learning (QML). More precisely, the integration of quantum computing with ML may lead to novel approaches that outperform classical algorithms by offering faster runtimes, better performance, and reduced data requirements. This advancement can benefit many areas such as computer vision, natural language processing, drug discovery, finance, and fundamental science.
As an interdisciplinary field, the development of QML requires close collabo-ration between leading scientists and engineers in both quantum computing and artificial intelligence (AI). At the same time, as QML advances alongside the continuous progress of quantum hardware, there is a growing need for expertise from the AI community to drive this emerging field forward. However, the distinct conceptual frameworks and terminologies of quantum and classical computing present significant barriers for researchers and practitioners with a classical ML background in understanding the mechanisms behind QML algorithms and the benefits they may offer. Reducing this barrier to entry remains a major challenge within the community.
To overcome this challenge, we have written this book to deliver a comprehensive introduction to the latest developments in QML. Whether you are an AI researcher, an ML practitioner, or a computer science student, this resource will equip you with a solid foundation in the principles and techniques of QML. By bridging the gap between classical ML and quantum computing, this book could serve as a useful resource for those looking to engage with QML and explore the forefront of AI in the quantum era.