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18/08/2017

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Tronserve admin

Friday 30th July 2021 01:58 AM

Google’s Quantum AI Lab has applied the newest generation of what D-Wave Systems describes as the world’s first commercial quantum computers. Quantum computing may probably solve particular problems a lot quicker than today’s normal computers while using reasonably less power to perform the calculations. Yet the energy efficiency of quantum computing however continues to be a mystery.

For the time being, D-Wave’s machines can scale up the number of quantum bits (qubits) they use without considerably increasing their power requirements. That is simply because D-Wave’s quantum computing hardware rely on a specific design consisting of metal niobium loops that act as superconductors when chilled to a frigid 15 millikelvin (-273° C). Much of the D-Wave hardware’s power consumption — a little lower than 25 kilowatts for the latest machine — goes toward running the refrigeration unit that keeps the quantum processor cool. The quantum processor itself requires a comparative pittance.

“The operation of the quantum processor itself requires remarkably little power—only a tiny fraction of a microwatt—which is essentially negligible in comparison to the power needs of the refrigerator and servers,” says Colin Williams, director of business development & strategic partnerships at D-Wave Systems.

The new 1000-qubit D-Wave 2X machine installed at Google’s lab has roughly double the qubits of its predecessor, the D-Wave Two machine. But the minimum amount of power used by the quantum processor means that “the total system power will still remain more or less constant for many generations to come” even as the quantum processor scales up to thousands of qubits, Williams says. D-Wave can currently get away with this because the same “cryostat” unit that uses so many kilowatts of power would still be enough to cool much larger quantum processors than the ones currently in use.

"It would be similar if you attach a large cooling device to your PC that uses many kilowatts of power — you would barely see an increase in power consumption when going to larger systems since the power is dominated by the large cooling infrastructure," says Matthias Troyer, a computational physicist at ETH Zurich.

The ability to scale up a D-Wave machine’s computing capabilities without maximizing its power consumption may sound appealing. But it really doesn’t say much about the power efficiency of quantum computing as opposed to classical computing. Today’s D-Wave machines perform about as well as a high-end PC on certain in depth tasks, but they use a lot more power based on their extreme cooling requirements. (High-end computing cores require just tens of watts of power.)

“While the ‘flat power requirement’ is a good statement to make for marketing, it is unclear at the moment what the true power needs are once the device is optimized and scaled up,” Troyer says. “Right now they need orders of magnitude more power than competing classical technology.”

However, it isn't exactly a fair comparison, Troyer says. “On the power side, they are currently losing,” he says. But the D-Wave machine “is not engineered to be power saving. It may pay off again at some point.”

Scott Aaronson, a theoretical computer scientist at MIT and a D-Wave critic, seemed bemused by the idea of D-Wave having a power advantage of any sort. Speaking about D-Wave’s reliance on a crygenic cooler he wrote in an email: “It’s amusing chutzpah to take such a gigantic difficulty and then present it as a feature.” He talked about that D-Wave could need an even more power-hungry cooling system to create lower temperatures that maximize its quantum processors’ chances of a “speedup” advantage over classical computing in the future.

D-Wave’s quantum annealing machines represent only one possible computer architecture for quantum computing. They are designed to solve a specialized set of “optimization problems” rather than act as universal logic-gate quantum computers. (The latter would be super-fast versions of today’s classical “gate-model” computers.) Google’s Quantum AI Lab has invested in both D-Wave’s machines and in looking into development of universal logic-gate quantum computers.

In the end, Troyer expects power requirements for quantum computing to most likely be “linearly proportional” to the number of qubits and their couplings, as well as proportional to the number of times operators must run and recool the system before it finds the solution.

Quantum computing’s huge benefits probably will not start to emerge until engineers build machines with many thousands or probably millions of qubits. That’s still a ways off even for D-Wave, which has chosen to scale up the number of qubits in its processors fairly quickly. Most quantum computing researchers have opted for a much slower approach of building quantum computing devices with just several qubits or tens of qubits, because of major challenges in correcting for qubit errors and maintaining coherence across the system.

However, both D-Wave and independent quantum computing labs share the same general goal of building machines that can exploit the “spooky physics” of quantum physics. Quantum computers are likely to perform many more calculations at the same time than classical machines. If quantum computers can conquer classical computers in terms of “time to solution,” they could also prove more power-efficient at the end of the day.

“If a quantum device can solve a problem with much better [time to solution] scaling than classical computing, it would also win on power," Troyer says.

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Posted by Tronserve admin

Google’s Quantum AI Lab has applied the newest generation of what D-Wave Systems describes as the world’s first commercial quantum computers. Quantum computing may probably solve particular problems a lot quicker than today’s normal computers while using reasonably less power to perform the calculations. Yet the energy efficiency of quantum computing however continues to be a mystery.

For the time being, D-Wave’s machines can scale up the number of quantum bits (qubits) they use without considerably increasing their power requirements. That is simply because D-Wave’s quantum computing hardware rely on a specific design consisting of metal niobium loops that act as superconductors when chilled to a frigid 15 millikelvin (-273° C). Much of the D-Wave hardware’s power consumption — a little lower than 25 kilowatts for the latest machine — goes toward running the refrigeration unit that keeps the quantum processor cool. The quantum processor itself requires a comparative pittance.

“The operation of the quantum processor itself requires remarkably little power—only a tiny fraction of a microwatt—which is essentially negligible in comparison to the power needs of the refrigerator and servers,” says Colin Williams, director of business development & strategic partnerships at D-Wave Systems.

The new 1000-qubit D-Wave 2X machine installed at Google’s lab has roughly double the qubits of its predecessor, the D-Wave Two machine. But the minimum amount of power used by the quantum processor means that “the total system power will still remain more or less constant for many generations to come” even as the quantum processor scales up to thousands of qubits, Williams says. D-Wave can currently get away with this because the same “cryostat” unit that uses so many kilowatts of power would still be enough to cool much larger quantum processors than the ones currently in use.

"It would be similar if you attach a large cooling device to your PC that uses many kilowatts of power — you would barely see an increase in power consumption when going to larger systems since the power is dominated by the large cooling infrastructure," says Matthias Troyer, a computational physicist at ETH Zurich.

The ability to scale up a D-Wave machine’s computing capabilities without maximizing its power consumption may sound appealing. But it really doesn’t say much about the power efficiency of quantum computing as opposed to classical computing. Today’s D-Wave machines perform about as well as a high-end PC on certain in depth tasks, but they use a lot more power based on their extreme cooling requirements. (High-end computing cores require just tens of watts of power.)

“While the ‘flat power requirement’ is a good statement to make for marketing, it is unclear at the moment what the true power needs are once the device is optimized and scaled up,” Troyer says. “Right now they need orders of magnitude more power than competing classical technology.”

However, it isn't exactly a fair comparison, Troyer says. “On the power side, they are currently losing,” he says. But the D-Wave machine “is not engineered to be power saving. It may pay off again at some point.”

Scott Aaronson, a theoretical computer scientist at MIT and a D-Wave critic, seemed bemused by the idea of D-Wave having a power advantage of any sort. Speaking about D-Wave’s reliance on a crygenic cooler he wrote in an email: “It’s amusing chutzpah to take such a gigantic difficulty and then present it as a feature.” He talked about that D-Wave could need an even more power-hungry cooling system to create lower temperatures that maximize its quantum processors’ chances of a “speedup” advantage over classical computing in the future.

D-Wave’s quantum annealing machines represent only one possible computer architecture for quantum computing. They are designed to solve a specialized set of “optimization problems” rather than act as universal logic-gate quantum computers. (The latter would be super-fast versions of today’s classical “gate-model” computers.) Google’s Quantum AI Lab has invested in both D-Wave’s machines and in looking into development of universal logic-gate quantum computers.

In the end, Troyer expects power requirements for quantum computing to most likely be “linearly proportional” to the number of qubits and their couplings, as well as proportional to the number of times operators must run and recool the system before it finds the solution.

Quantum computing’s huge benefits probably will not start to emerge until engineers build machines with many thousands or probably millions of qubits. That’s still a ways off even for D-Wave, which has chosen to scale up the number of qubits in its processors fairly quickly. Most quantum computing researchers have opted for a much slower approach of building quantum computing devices with just several qubits or tens of qubits, because of major challenges in correcting for qubit errors and maintaining coherence across the system.

However, both D-Wave and independent quantum computing labs share the same general goal of building machines that can exploit the “spooky physics” of quantum physics. Quantum computers are likely to perform many more calculations at the same time than classical machines. If quantum computers can conquer classical computers in terms of “time to solution,” they could also prove more power-efficient at the end of the day.

“If a quantum device can solve a problem with much better [time to solution] scaling than classical computing, it would also win on power," Troyer says.

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quantum devices
quantum computer
quantum computing

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