Ferenc Huszár

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Image Super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsam-pled image. Most current single image SR methods use empirical risk minimi-sation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such methods tend to be blurry,(More)
In this paper we revisit the problem of optimal design of quantum tomographic experiments. In contrast to previous approaches where an optimal set of measurements is decided in advance of the experiment, we allow for measurements to be adaptively and efficiently reoptimized depending on data collected so far. We develop an adaptive statistical framework(More)
We report an experimental realization of an adaptive quantum state tomography protocol. Our method takes advantage of a Bayesian approach to statistical inference and is naturally tailored for adaptive strategies. For pure states, we observe close to N −1 scaling of infidelity with overall number of registered events, while the best nonadaptive protocols(More)
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