Country for PR: United Kingdom
Contributor: PR Newswire Europe
Tuesday, March 30 2021 - 18:00
AsiaNet
Cambridge Quantum Computing Pioneers Quantum Machine Learning Methods for Reasoning
CAMBRIDGE, England, March 30, 2021 /PRNewswire-AsiaNet/ --

    - Quantum-assisted reasoning based on partial information demonstrates 
quantum machine intelligence that is accurate, flexible, and effective


    Scientists at Cambridge Quantum Computing (CQC) 
[https://cambridgequantum.com/] 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.

    In this paper [https://arxiv.org/abs/2103.06720] 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.

    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.

    Machine learning scientists across industries and quantum software and 
hardware developers are the groups of researchers that should benefit the most 
from this development in the near-term.

    This Medium article 
[https://medium.com/cambridge-quantum-computing/reasoning-under-uncertainty-with
-a-near-term-quantum-computer-99882dc04bb] accompanies the scientific paper and 
provides an accessible exposition of the principles behind this pioneering 
work, as well as descriptions of the proofs of principle implemented by the 
team.

    With quantum devices set to improve in the coming years, this research lays 
the groundwork for quantum computing to be applied to probabilistic reasoning 
and its direct application in engineering and business-relevant problems.

    In this video 
[https://www.youtube.com/watch?v=kMNTHkb627c&feature=youtu.be], Dr. Mattia 
Fiorentini, Head of our Quantum Machine Learning division, provides detailed 
insight on the project outcomes and its implications.

    About Cambridge 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 offices in the UK, USA and Japan. For more information, visit 
CQC at http://www.cambridgequantum.com and on LinkedIn 
[https://www.linkedin.com/uas/login?session_redirect=https%3A%2F%2Fwww.linkedin.
com%2Fcompany%2F21661539%2Fadmin%2F]. Access the tket Python module on GitHub 
[https://cqcl.github.io/pytket/build/html/index.html].

    Source: Cambridge Quantum Computing