D-Wave Systems has used quantum annealing to do simulations of quantum magnetic phase transitions. The company claims that some of their calculations would be beyond the capabilities of the most powerful conventional (classical) computers – an achievement referred to as quantum advantage. This would mark the first time quantum computers had achieved such a feat for a practical physics problem.
However, the claim has been challenged by two independent groups of researchers in Switzerland and the US, who have published papers on the arXiv preprint server that report that similar calculations could be done using classical computers. D-Wave’s experts believe these classical results fall well short of the company’s own accomplishments, and some independent experts agree with D-Wave.
While most companies trying to build practical quantum computers are developing “universal” or “gate model” quantum systems, US-based D-Wave has principally focused on quantum annealing devices. While such systems are less programmable than gate model systems, the approach has allowed D-Wave to build machines with many more quantum bits (qubits) than any of its competitors. Whereas researchers at Google Quantum AI and researchers in China have, independently, recently unveiled 105-qubit universal quantum processors, some of D-Wave’s have more than 5000 qubits. Moreover, D-Wave’s systems are already in practical use, with hardware owned by the Japanese mobile phone company NTT Docomo being used to optimize cell tower operations. Systems are also being used for network optimization at motor companies, food producers and elsewhere.
Trevor Lanting, the chief development officer at D-Wave, explains the central principles behind quantum-annealing computation: “You have a network of qubits with programmable couplings and weights between those devices and then you program in a certain configuration – a certain bias on all of the connections in the annealing processor,” he says. The quantum annealing algorithm places the system in a superposition of all possible states of the system. When the couplings are slowly switched off, the system settles into its most energetically favoured state – which is the desired solution.
Quantum hiking
Lanting compares this to a hiker in the mountains searching for the lowest point on a landscape: “As a classical hiker all you can really do is start going downhill until you get to a minimum, he explains; “The problem is that, because you’re not doing a global search, you could get stuck in a local valley that isn’t at the minimum elevation.” By starting out in a quantum superposition of all possible states (or locations in the mountains), however, quantum annealing is able to find the global potential minimum.
In the new work, researchers at D-Wave and elsewhere set out to show that their machines could use quantum annealing to solve practical physics problems beyond the reach of classical computers. The researchers used two different 1200-qubit processors to model magnetic quantum phase transitions. This is a similar problem to one studied in gate-model systems by researchers at Google and Harvard University in independent work announced in February.
“When water freezes into ice, you can sometimes see patterns in the ice crystal, and this is a result of the dynamics of the phase transition,” explains Andrew King, who is senior distinguished scientist at D-Wave and the lead author of a paper describing the work. “The experiments that we’re demonstrating shed light on a quantum analogue of this phenomenon taking place in a magnetic material that has been programmed into our quantum processors and a phase transition driven by a magnetic field.” Understanding such phase transitions are important in the discovery and design of new magnetic materials.
Quantum versus classical
The researchers studied multiple configurations, comprising ever-more spins arranged in ever-more complex lattice structures. The company says that its system performed the most complex simulation in minutes. They also ascertained how long it would take to do the simulations using several leading classical computation techniques, including neural network methods, and how the time to achieve a solution grew with the complexity of the problem. Based on this, they extrapolated that the most complex lattices would require almost a million years on Frontier, which is one of the world’s most powerful supercomputers.
However, two independent groups – one at EPFL in Switzerland and one at the Flatiron Institute in the US – have posted papers on the arXiv preprint server claiming to have done some of the less complex calculations using classical computers. They argue that their results should scale simply to larger sizes; the implication being that classical computers could solve the more complicated problems addressed by D-Wave.
King has a simple response: “You don’t just need to do the easy simulations, you need to do the hard ones as well, and nobody has demonstrated that.” Lanting adds that “I see this as a healthy back and forth between quantum and classical methods, but I really think that, with these results, we’re pulling ahead of classical methods on the biggest scales we can calculate”.
Very interesting work
Frank Verstraete of the University of Cambridge is unsurprised by some scientists’ scepticism. “D-Wave have historically been the absolute champions at overselling what they did,” he says. “But now it seems they’re doing something nobody else can reproduce, and in that sense it’s very interesting.” He does note, however, that the specific problem chosen is not, in his view an interesting one from a physics perspective, and has been chosen purely to be difficult for a classical computer.
Daniel Lidar of the University of Southern California, who has previously collaborated with D-Wave on similar problems but was not involved in the current work, says “I do think this is quite the breakthrough…The ability to anneal very fast on the timescales of the coherence times of the qubits has now become possible, and that’s really a game changer here.” He concludes that “the arms race is destined to continue between quantum and classical simulations, and because, in all likelihood, these are problems that are extremely hard classically, I think the quantum win is going to become more and more indisputable.”
The D-Wave research is described in Science. The Flatiron Institute preprint is by Joseph Tindall and colleagues, and the EPFL preprint is by Linda Mauron and Giuseppe Carleo.
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