Cambridge Quantum Computing Develops Quantum Machine Learning Methods for Reasoning
(HPCWire) Scientists at Cambridge Quantum Computing (CQC) 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.
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.
The paper implements three proofs of principle on simulators and on an IBM Q quantum computer to demonstrate quantum-assisted reasoning on:
inference on random instances of a textbook Bayesian network
inferring market regime switches in a hidden Markov model of a simulated financial time series
a medical diagnosis task known as the “lung cancer” problem.
The proofs of principle suggest quantum machines using highly expressive inference models could enable new applications in diverse fields. The paper draws on the fact that sampling from complex distributions is considered among the most promising ways towards a quantum advantage in machine learning with today’s noisy quantum devices. This pioneering work indicates how quantum computing, even in its current early stage, is an effective tool for studying science’s most ambitious questions such as the emulation of human reasoning.