Editor’s note: This article was originally published by Ranconteur in an “AI for Business” special report, published in The Times on Sunday, April 28, 2020.

Machine-learning and quantum computing are two technologies that have incredible potential in their own right. Now researchers are bringing them together. The main goal is to achieve a so-called quantum advantage, where complex algorithms can be calculated significantly faster than with the best classical computer. This would be a game-changer in the field of AI.

Such a breakthrough could lead to new drug discoveries, advances in chemistry, as well as better data science, weather predictions and natural-language processing. “We could be as little as three years away from achieving a quantum advantage in AI if the largest players in the quantum computing space meet their goals,” says Ilyas Khan, chief executive of Cambridge Quantum Computing.

This comes after Google announced late last year that it had achieved “quantum supremacy”, claiming their quantum computer had cracked a problem that would take even the fastest conventional machine thousands of years to solve.

“Developing quantum machine-learning algorithms could allow us to solve complex problems much more quickly. To realise the full potential of quantum computing for AI, we need to increase the number of qubits that make up these systems,” says Dr Jay Gambetta, vice president of quantum computing at IBM Research.

Quantum devices exploit the strange properties of quantum physics and mechanics to speed up calculations. Classical computers store data in bits, as zeros or ones. Quantum computers use qubits, where data can exist in two different states simultaneously. This gives them more computational fire power. We’re talking up to a million times faster than some classical computers.

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