Hi all,
Tomorrow Kai Ott will tell us about his semester project with Mischa Woods and Joe Renes, entitled 'Understanding Quantum Clocks: Measures of Precision and Entropy Production'. See below for the abstract. The talk will take place at 2pm in HIT F 32 or on Zoom: https://ethz.zoom.us/j/362994444.
Best,
Ladina
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Recent work on quantum clocks has shown quantum-over-classical advantage in time-keeping. In particular, in the limit of large dimensions, quantum ticking clocks can achieve arbitrary precision $R_1$ at vanishing entropy production $\Sigma_1$ per tick, while for classical clocks there is a fundamental trade-off between precision and entropy production per tick, i.e. for any desired precision, there is an associated minimal entropy production. In this work, we show that another type of precision $I_1$ defined via the relative entropy can be made arbitrarily large at vanishing entropy production even for classical ticking clocks of finite dimension. Then, we investigate optimal classical clocks, which achieve the minimum entropy production per tick for a given precision $R_1$. We give closed form solutions for their precision, discuss symmetries, and state how their parameters should be chosen to truly be optimal. Lastly, we relate the classical case to the quantum case to explain the approximate quadratic precision advantage of the latter over the former.
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.