Quantum Musings

Omer Subasi

In this work, we address the problem of automating quantum variational machine learning. We develop a multi-locality parallelizable search algorithm, called MUSE, to find the initial points and the sets of parameters that achieve the best performance for quantum variational circuit learning. Simulations with five real-world classification datasets indicate that on average, MUSE improves the detection accuracy of quantum variational classifiers 2.3 times with respect to the observed lowest scores. Moreover, when applied to two real-world regression datasets, MUSE improves the quality of the predictions from negative coefficients of determination to positive ones. Furthermore, the classification and regression scores of the quantum variational models trained with MUSE are on par with the classical counterparts.

Dirk Kreimer

We report on some conceptual changes in our present understanding of Quantum Field Theory and muse about possible consequences for the understanding of v > c signals.

Anthony J. Killard

David Finkelstein

Dirk Kreimer

Abstract We report on some conceptual changes in our present understanding of Quantum Field Theory and muse about possible consequences for the understanding of v > c signals.