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تسجيل مستخدم جديدAn introduction to quantum machine learning
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Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessable way, and discusses the potential of a future theory of quantum learning.
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The following notes are based on lectures delivered at the research school Modeling and Control of Open Quantum Systems (Mod{e}lisation et contr^{o}le des syst`{e}mes quantiques ouverts) at CIRM, Marseille, 16-20 April, 2018, as part of the Trimester
textit{Measurement and Control of Quantum Systems: Theory and Experiments} organized at Institut Henri Poincar{e}, Paris, France. The aim is to introduce quantum filtering to an audience with a background in either quantum theory or classical filtering.
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We describe some basic tools in the spectral theory of Schrodinger operator on metric graphs (also known as quantum graph) by studying in detail some basic examples. The exposition is kept as elementary and accessible as possible. In the later sectio
ns we apply these tools to prove some results on the count of zeros of the eigenfunctions of quantum graphs.
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