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Scientists find a way to bridge the gap between classical and quantum computing



Quantum computers are hundreds of millions times more powerful than today’s computers, and this breakthrough could put one on your desktop.


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Quantum computers that are millions of times more powerful than traditional computing platforms albeit under certain conditions, exist today, and Google’s own quantum computer recently passed a milestone called quantum supremacy to become the world’s most powerful computer. But all that that said, these quantum computers are still limited, cut down versions of what we hope fully blown quantum computers are going to be able to do in the future.


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Now though researchers have developed hardware for a “Probabilistic computer,”  a device that might be able to bridge the gap between genuine quantum computers, that have to operate at near sub zero temperatures in labs, and the standard PCs and laptops we have today running at room temperature on our desks.

The special trick that a probabilistic computer can do is to solve quantum problems without actually going quantum, as it were. It does this using something called a p-bit, which the team at Purdue University in the US behind this research describes as a “Poor man’s qubit”.

Whereas classical computing bits can store information as either a 1 or a 0, qubits, that are used by quantum computers, can be both at the same time, and that means a big leap in processing power. A p-bit, meanwhile, can only be a 1 or a 0, but they can switch between those two states very, very fast.


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By carefully controlling these fluctuations, the researchers probabilistic computer was able tackle calculations of a kind that are generally considered to be quantum computing problems, but without the need to use an actual quantum computer.

As an added bonus, p-bits work at room temperature, whereas qubits need super-cold conditions to operate properly, so they’re easier to adapt into existing computers.

“There is a useful subset of problems solvable with qubits that can also be solved with p-bits,” says electrical and computer engineer Supriyo Datta, from Purdue. “You might say that a p-bit is a ‘poor man’s qubit’.”


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The result of the research was a modified Magnetoresistive Random-Access Memory (MRAM) device, used to store information in some of today’s computers. Magnetic orientations are used to create states of resistance that represent 1s or 0s.

Eight of these custom-made MRAM p-bit units were put together with a controller chip to form a probabilistic computer – one where units are only likely to adopt a specific value, which is where the “probabilistic” name comes from.

The researchers were then able to solve integer factorisation problems, where numbers are broken down into smaller multiples. This is usually considered a quantum problem, one that quantum computers would excel at – classical computers can do it, just more slowly and less efficiently.


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This probabilistic computer and its p-bits represents some sort of middle ground between the two. The researchers suggest that fully realised p-bit machines would handle integer factorisation and similar problems using less space and energy than today’s computers.

“On a chip, this circuit would take up the same area as a transistor, but perform a function that would have taken thousands of transistors to perform,” says Datta. “It also operates in a manner that could speed up calculation through the parallel operation of a large number of p-bits.”

This machine is going to have to be scaled up and refined further to be of practical use, but the researchers think those advancements might not be too far off. These devices can then take over from classical computers for certain problems, until the quantum computing revolution finally arrives.


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Scientists are making progress, but there’s still some way to go before qubits are stable and practical enough to actually run the sums we need them to run and to scale up properly, and making qubits and connecting them together remains a tough challenge.

“In the near future, p-bits could better help a machine to learn like a human does or optimise a route for goods to travel to market,” added Datta.

The research has been published in Nature.

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