Quantum-assisted reasoning based on partial information demonstrates quantum machine intelligence that is accurate, flexible and effective
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 have established that quantum computers can learn to deal with the uncertainty that is typical of real-world scenarios and which humans can often handle intuitively.
Three proofs of principle on simulators and an IBM Q quantum computer 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 finding 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.
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.
