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Machine learning algorithm could change how we see quantum systems

Machine learning algorithm helps unravel physics underlying quantum systems

The nitrogen vacancy centre set-up, that was used for the first experimental demonstration of QMLA. Some authors of the paper: Rafaelle Santagati (top left); Andreas Gentile (top right); Sebastian Knauer (bottom left); Brian Flynn (bottom right). Scientists from the University’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems – paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation. In physics, systems of particles and their evolution are described by mathematical models, requiring the successful interplay of theoretical arguments and experimental verification. Even more complex is the description of systems of particles interacting with each other at the quantum mechanical level, which is often done using a Hamiltonian model. The process of formulating Hamiltonian models from observations is m

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