Hi all,
Tomorrow Manuel John will tell us about his master thesis at IBM, entitled 'Optimizations of Quantum Classification Algorithms’. See below for the abstract. The talk will take place at 2pm in HIT J 53 or on Zoom: https://ethz.zoom.us/j/362994444.
Best, Ladina
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Computers are well known for their ability to sieve through vast amounts of data. They are ubiquitous in today’s world, as specially designed algorithms have the ability to analyze, understand, and group data and ideas into distinct categories. Naturally, promises arising from provable speed-ups of quantum algorithms in relevant problem settings also motivate the search for quantum machine learning algorithms. In this thesis, we embed data in quantum states with the use of parametrized quantum circuits, which act as mappings of the original data to Hilbert space. We then compare the datapoints by evaluating the fidelity between embedded datapoints and improve existing classifiers by evaluating functions of the fidelity, which introduce non-linear decision boundaries in Hilbert space. This allows us to classify ensembles of pure states and mixed states, and we achieve results on real-world datasets competitive with those of a classical support vector machine.