The future of supercomputers could be powered by ‘magic dust’ made of light and matter

Strange quantum particles made of half light and half matter could be the key to solving some of the world’s most complex problems. 

The future of supercomputers could be powered by ‘magic dust’ made of light and matter

Dubbed “magic dust” by researchers from Cambridge University, these strange polariton particles occur as a result of the behaviour of other material and could help power the next generation of supercomputers. In fact, this dust could help computer scientists surpass the capabilities of even the most powerful supercomputers that exist today, helping them tackle simulations not currently thought possible. 

What is “magic dust”?

Magic dust consists of polaritons which are a form of quasiparticles, meaning they are not particles in themselves but rather they occur as the result of the behaviour of a material. In terms of describing their behaviour, they can be considered particles in their own right, though. 

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For example, if there is an atomic structure where, at one point, there could be a negatively charged electron but there isn’t one, this causes a positive charge overall. This lack of electron can be considered something in itself, and is called an electron hole.

The “magic dust” is made by shining a laser at stacked layers of atoms including gallium, arsenic, indium, and aluminium.

When the electrons in the atoms absorb light, they gain energy which is then lost again through emitting photons at which point they shine at a specific colour. In the new study, the researchers showed how to create polaritons such that they find the minimum solution to a problem. This path of least resistance-style behaviour forms the very basis of supercomputers to help them run greater numbers of calculations and simulations. As a result, magic dust has the potential to be much faster than classical computing, the researchers say.

How will magic dust power future supercomputers? 

All computing involves arriving at a solution to a problem using the fewest steps possible. 

The search for an optimal solution is likened to looking for the lowest point in a mountainous terrain with valleys, trenches, and drops. As the researchers explain, a hiker may go downhill and think they have reached the lowest point of the entire landscape, yet there may be a deeper drop just behind the next mountain – they can’t possibly know without scaling that second mountain, and so on. Such a search may seem daunting in natural terrain, and its complexity increases in high-dimensional space used in computing. 

“This is exactly the problem to tackle when the objective function to minimise represents a real-life problem with many unknowns, parameters, and constraints,” said Professor Natalia Berloff from Cambridge University, the paper’s first author.

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The authors took a completely new stance to tackling this kind of problem. Instead of moving along the terrain in search of the deepest point, they decided to ‘fill’ the landscape with a ‘magical dust’ that only shines at the lowest level. This dust shows the location of all the possible solutions making it easier to work through them until the optimum answer is reached. This, in theory, speeds up how many calculations a supercomputer can do per second and makes them work more efficiently, saving energy and power – both barriers to entry for the largest supercomputers. 

“A few years ago our purely theoretical proposal on how to do this was rejected by three scientific journals,” said Berloff. “One referee said, ‘Who would be crazy enough to try to implement this?!’ So we had to do it ourselves, and now we’ve proved our proposal with experimental data.”

“We are just at the beginning of exploring the potential of polariton graphs for solving complex problems,” said co-author Professor Pavlos Lagoudakis, from the University of Southampton.

“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.”

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