One of the exciting areas lighting up the world of quantum computing is that of Quantum Machine Learning. With the massive interest in classical machine learning which has affected all areas of our lives and threatens even more disruption such as self driving cars and automation beyond imagination, is it no wonder that researchers look to utilise the inherent power of Quantum Computing to drive innovation in reasoning and machine learning. The announcement from CQC (Cambridge Quantum Computing) highlights how quantum machine can learn to infer hidden information from very general probabilistic reasoning models.
Published in arXiv, machine learn to infer hidden information from very general probabilistic reasoning models. This is especially useful for real-world scenarios where humans are having to use intuition to solve such problems. Typically applications could be medical diagnosis, machine fault-detection in mission-critical applications and even in the investment sector with financial forecasting. Of course the applications listed here are just the tip of the iceberg and like classical machine learning, applications are likely to emerge as researchers see the potential of any new technique and technology.
The research was led by Dr. Marcello Benedetti with co-authors Brian Coyle, Dr. Michael Lubasch, and Dr. Matthias Rosenkranz. The published work describes the implementation of three proofs of principle on simulators and on an IBM Q quantum computer to demonstrate quantum-assisted reasoning. Three main elements of the paper demonstrate: inference on random instances of a Bayesian network, inferring market regime switches in a hidden Markov model of a simulated financial time series and a medical diagnosis task.
The proofs of principle published are further evidence that quantum computing is a general-purpose technology. The application of which in time can be used to model uncertainty in complex scenarios. Compared to classical machine learning, the computing and energy costs required to handle complex decision-making algorithms using the probabilistic inferences needed to model uncertainty are lower energy and less expensive. In summary: Quantum-assisted reasoning based on partial information demonstrates quantum machine intelligence that is effective
and resource-efficient
Quantum-assisted reasoning based on partial information demonstrates quantum machine intelligence that is effective and resource-efficient
Cambridge Quantum Computing
Cambridge Quantum Computing
One of the Granddaddy’s of Quantum Computing, founded in 2014 and backed by some of the world’s leading quantum computing companies. CQC is a global leader in quantum software and quantum algorithms, enabling clients to achieve the most out of rapidly evolving quantum computing hardware. CQC has a global footprint with offices in the UK, USA and Japan. For more information, visit CQC for more information on the company and its open source Quantum Computing language named t|ket>.
