Quantum Machines Can Learn to Reason, Researchers Say
- By John K. Waters
Researchers at quantum software developer Cambridge Quantum Computing (CQC) have shown that quantum computers can learn to reason, even under conditions of partial information and uncertainty that humans typically handle intuitively, the company says.
The CQC researchers offered proof of their claim in a new paper ("Variational inference with a quantum computer") that quantum machines can learn to infer hidden information from very general probabilistic reasoning models.
"Reasoning" is this context means inferring conclusions from premises and available information. The grass is wet, so it probably rained.
The methods outlined by the researchers could improve the performance of a range of applications in which reasoning in complex systems and quantifying uncertainty are crucial. Examples include medical diagnosis, fault-detection in mission-critical machines, and financial forecasting for investment management.
In the paper, which was published on the pre-print repository arXiv, CQC researchers Marcello Benedetti, Brian Coyle, Michael Lubasch, and Matthias Rosenkranz, who are part of the Quantum Machine Learning division of CQC headed by Mattia Fiorentini, document the implementation of 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 paths to a quantum advantage in machine learning with today’s noisy quantum devices.
Quantum "noise" can arise from control electronics, heat, or impurities in the materials in the quantum bits (qubits) themselves. This is generally considered a downside of quantum computing, because environmental noise destroys the fragile quantum state of qubits, and typically impedes quantum computer use.
"We envision that machine learning scientists as well as quantum software and hardware developers will likely be the groups most interested in this line of research," said Rosenkranz, in a Medium post. "The first group may find these tools useful for tackling ambitious problems, such as studying reasoning and cause-effect in complex systems. The second group may apply our work to uncover new applications of near-term quantum computers in science engineering and business—even opportunities for quantum advantage."
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, the company says.
John K. Waters is the editor in chief of a number of Converge360.com sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS. He can be reached at email@example.com.