Cambridge Quantum Computing has demonstrated Variational Inference on quantum computers for the first time. Results are encouraging, indicating a promising new approach in quantum machine learning.
The applications and development of quantum computers have steadily picked up pace in the last few years. We’ve seen researchers applying this novel method of computation in a variety of domains including quantum chemistry, fluid dynamics research, open problems, and even machine learning, all with promising results. Continuing this trend, UK-based startup Cambridge Quantum Computing (CQC) has now demonstrated that quantum computers «can learn to reason». Confusing at first, this claim is based upon new research coming out of CQC. Dr. Mattia Fiorentini, Head of Quantum Machine Learning at the firm, and his team of researchers investigated using quantum computers for Variational Inference. Variational Inference is a process through which we approximate a given probability distribution using stochastic optimization and other learning techniques. Jargon aside, this means a quantum computer outputs potential solutions to inferential questions such as that given the grass is wet and it’s cloudy, what’s the more probable cause for it? Rain or sprinklers? Formally, the question is posed as follows: The team’s work, titled Variational inference with a quantum computer, has been published on the pre-print repository arXiv and highlights what the firm believes to be a promising indicator that quantum computers are great for Variational Inference, and by extension, at reasoning.
