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Date Time Presenting… Valeria Saggio: Quantum physics for machine learning The bridge between artificial intelligence and quantum mechanics is currently one of the most investigated topics in the academic community. Over the past few years, many academics have started to study the ways in which quantum mechanics can prove beneficial for learning robots, or vice versa. Yet, robots were still incapable of learning faster. Robots learn faster when quantum physics is used Valeria Saggio, doctoral candidate at the Vienna Doctoral School in Physics, is working on this hot topic and does experiments with photons. Together with international collaboration partners in Austria, Germany, the Netherlands, and the USA, she was able to show that quantum technology can speed-up the learning process. “In one of our latest experiments, we were able to demonstrate that agents can learn faster. The learning time can be reduced when quantum physics is used in the machine learning ....
E-Mail Robots solving computer games, recognizing human voices, or helping in finding optimal medical treatments: those are only a few astonishing examples of what the field of artificial intelligence has produced in the past years. The ongoing race for better machines has led to the question of how and with what means improvements can be achieved. In parallel, huge recent progress in quantum technologies have confirmed the power of quantum physics, not only for its often peculiar and puzzling theories, but also for real-life applications. Hence, the idea of merging the two fields: on one hand, artificial intelligence with its autonomous machines; on the other hand, quantum physics with its powerful algorithms. ....
Just Super/Getty Images Machine learning, a process used to train artificial intelligences, can take an extremely long time – but a quantum trick could massively speed things up for tasks involving particles of light called photons. In reinforcement learning, an algorithm runs through the same problem over and over again and is given a numerical reward only when it reaches the correct answer. That process teaches it to find the correct answer more quickly when pitted against similar problems later on. Advertisement Now Valeria Saggio at the University of Vienna in Austria and her colleagues have added a quantum twist to accelerate this process. They set up an experiment involving a photon moving through a wave guide and ending up in one of four possible states. They tasked an AI with making sure the photon ended up in one particular state, and rewarded it for doing so. ....