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Zooming in on a wafer of D-Wave Quantum Computers Bayers Factors In the paper entitled Learning models of quantum systems from experiments, published in Nature Physics, quantum mechanics from QET Labs of Bristol are describing an algorithm that overcomes such challenges by functioning as an independent agent, through the use of machine learning to oppose or contradict engineer Hamiltonian models. Essentially, the research team developed a new protocol for the formulation and validation of approximate models for quantum systems of interest. As indicated in their study, the researchers algorithm is working independently, designing and carrying out experiments on the aimed quantum system, with the resulting data being fed back into the algorithm.

Wikimedia commonsBristol qetlabsAnthony laingSteve jurvetsonBrian flynnAndreas gentileDepartment of statisticsUniversity of bristol quantum engineering technology labsQu coProfessor in school of physics bristolUniversity of bristol qetlabsQuantum engineering centerUniversity of washington as bayersQuantum computersQuantum engineering technology labsGiant magellan telescope