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New “magic dust” combining light and matter could power enhanced supercomputing

Russian and UK researchers have demonstrated that “polaritons,” a type of subatomic particles that are “half-light and half-matter,” can facilitate computational problem solving the likes of which even the most powerful supercomputers today cannot achieve. In a report in the journal Nature Materials, the researchers suggested that these particles’ computing applications could solve some currently unsolvable problems in biology, finance, space travel, and other fields.

 

Polaritons are ten-thousand times lighter than electrons and form when a laser shines upon stacked layers of selected atoms such as arsenic, gallium, indium, and aluminium. The electrons in the layers absorb the light and emit polaritons. And when enough polaritons are present, they can condense and form a quantum object that human-built instruments can detect through photoluminescence measurements.

 

“We are just at the beginning of exploring the potential of polariton graphs for solving complex problems,” said co-author Professor Pavlos Lagoudakis, Head of the Hybrid Photonics Lab at the University of Southampton and the Skolkovo Institute of Science and Technology, where the experiments were performed. “We are currently scaling up our device to hundreds of nodes, while testing its fundamental computational power. The ultimate goal is a microchip quantum simulator operating at ambient conditions.”

 

The researchers explained that all computer-based problem solving, no matter the system, works by finding the solution that has the fewest steps.

The path with the fewest steps toward a solution is harder to calculate when a problem is very complex or has underlying subsets of interconnected problems. Modern supercomputers can work through only limited subsets of such problems.

Lagoudakis and colleagues think that polaritons can help computers work around this. The computer might create polaritons that condense and undergo quantum phases that create a configuration corresponding to the simplest solution to the problem it is trying to solve.

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