{"id":25940,"date":"2021-04-11T06:00:00","date_gmt":"2021-04-11T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=25940"},"modified":"2021-04-12T11:10:33","modified_gmt":"2021-04-12T18:10:33","slug":"cambridge-quantum-computing-pioneers-quantum-machine-learning-methods-for-reasoning","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/04\/11\/cambridge-quantum-computing-pioneers-quantum-machine-learning-methods-for-reasoning\/","title":{"rendered":"Cambridge Quantum Computing Pioneers Quantum Machine Learning Methods for Reasoning"},"content":{"rendered":"\n

Scientists at Cambridge Quantum Computing (CQC<\/a>) have developed methods and demonstrated that quantum machines can learn to infer hidden information from very general probabilistic reasoning models. These methods could improve a broad range of applications, where reasoning in complex systems and quantifying uncertainty are crucial. Examples include medical diagnosis, fault-detection in mission-critical machines, or financial forecasting for investment management.<\/p>\n\n\n\n

In this paper<\/a> published on the pre-print repository arXiv, CQC researchers established that quantum computers can learn to deal with the uncertainty that is typical of real-world scenarios, and which humans can often handle in an intuitive way. The research team has been led by Dr. Marcello Benedetti with co-authors Brian Coyle, Dr. Michael Lubasch, and Dr. Matthias Rosenkranz, and is part of the Quantum Machine Learning division of CQC, headed by Dr. Mattia Fiorentini.<\/p>\n\n\n\n

The paper implements three proofs of principle on simulators and on an IBM Q quantum computer to demonstrate quantum-assisted reasoning on:<\/p>\n\n\n\n