Quantum machine learning for particle physics using a variational quantum classifier

Quantum Physics: A Case for Anti-Realism?

quantum physics: a case for anti-realism?

the aim of the present paper is to sbo» that anti-realism in science, 1vhich has a1;guab/y become more fashionable in recentyears, contrary to tuha: its proponents assert, cannot make use of quantum mechanics and its impressiue achievements as a frump card in justifying its claims. i will argue that scientific anti-realism far from providing scientific communiry with a progressive methodologica...

Enhanced Quantum Synchronization via Quantum Machine Learning

F. A. Cárdenas-López,1, 2, ∗ M. Sanz,3, † J. C. Retamal,1, 2 and E. Solano3, 4 Departamento de Fı́sica, Universidad de Santiago de Chile (USACH), Avenida Ecuador 3493, 9170124, Santiago, Chile Center for the Development of Nanoscience and Nanotechnology 9170124, Estación Central, Santiago, Chile Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilba...

Quantum adiabatic machine learning

We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. In the training phase we identify an optimal set of weak classifiers, to form a single strong classifier. In the testing phase we adiabatically evolve one or more strong classifiers on a superposition of inputs in order to find certain anomalous elements in the classification space. Both the traini...

Quantum-enhanced machine learning

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all t...