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Natalia Ares from the University of Oxford presents machine learning for tackling quantum device variability at the 7th International Conference on Quantum Techniques in Machine Learning, held at CERN in November. More than 300 people attended in person, with more following online. (Image: CERN) Problem solving gets faster if quantum methodologies are used instead of classical computers. Physicists and computer scientists are therefore working closely together to explore this potential. In November, the 7th International Conference on Quantum Techniques in Machine Learning (QTML) was held at CERN, bringing together more than 300 researchers and industry partners in the field. Machine learning uses data and algorithms to help computers to learn patterns and perform more effectively at tasks ranging from helping doctors to diagnose cancer to improving facial recognition. Combining techniques from quantum physics with machine learning can reduce the number of steps needed for algorithms t

Miguel marquinaMichele grossiUniversity of melbourneInternational conference on quantum techniques in machineUniversity of oxfordQuantum instituteQuantum technology initiativeNatalia aresInternational conferenceQuantum techniquesMachine learningOpen quantum instituteArge hadron colliderHigh energy physics