If the age of quantum computing dawned 3 years ago, its rising sun may have hidden behind a cloud. In 2019, Google researchers claimed to have passed a milestone known as quantum supremacy when their Sycamore quantum computer performed in 200 seconds an obscure calculation that they believe would connect a supercomputer for 10,000 years. Now scientists in China have done the calculation in a few hours with ordinary processors. They say a supercomputer could beat Sycamore outright.
“I think they’re right that if they had access to a big enough supercomputer, they could simulate … the task in a matter of seconds,” said Scott Aronson, a computer scientist at the University of Texas at Austin. The progress takes some of the shine off Google’s claim, says Greg Kuperberg, a mathematician at the University of California, Davis. “Getting within 300 feet of the top is less exciting than getting to the top.”
Still, the promise of quantum computing remains intact, Kuperberg and others say. And Sergio Boixo, chief scientist for Google Quantum AI, said in an email that the Google team knows its advantage may not last very long. “In our 2019 paper, we said that classical algorithms would improve,” he said. But “we don’t think this classical approach can handle quantum circuits in 2022 and beyond.”
The “problem” solved by Sycamore is designed to be difficult for a conventional computer, but as easy as possible for a quantum computer that manipulates qubits that can be set to 0, 1, or—thanks to quantum mechanics—any combination of 0 and 1 at the same time. Together, Sycamore’s 53 qubits, tiny resonating electrical circuits made of superconducting metal, can encode any number from 0 to 253 (roughly 9 quadrillion), or even all of them at once.
Starting with all qubits set to 0, the Google researchers applied a random but fixed set of logic operations, or gates, to single qubits and pairs over 20 cycles, then read the qubits. Roughly speaking, quantum waves representing all possible outputs spilled between the qubits and gates, creating perturbations that amplified some outputs and canceled others. So some must have appeared more likely than others. Over millions of trials, a pattern of spicy results emerged.
The Google researchers say that simulating these perturbation effects would outpace even Summit, a supercomputer at Oak Ridge National Laboratory that has 9,216 central processing units and 27,648 faster graphics processing units (GPUs). IBM researchers who developed Summit quickly countered that if they used every bit of hard drive available to the computer, it could handle the calculations for several days. Now Pan Zhang, a statistical physicist at the Institute of Theoretical Physics at the Chinese Academy of Sciences, and his colleagues have shown how to defeat Sycamore in an article in Physical Review Letters.
Following others, Zhang and colleagues recast the problem as a 3D mathematical array called a tensor mesh. It consists of 20 layers, one for each cycle of gates, with each layer consisting of 53 points, one for each qubit. Lines connected the points to represent doors, with each door encoded in a tensor—a 2D or 4D grid of complex numbers. Running the simulation then boils down to, essentially, multiplying all the tensors. “The advantage of the tensor mesh method is that we can use many GPUs to do the calculations in parallel,” Zhang says.
Zhang and his colleagues also relied on a key insight: Sycamore’s calculations were far from accurate, so theirs shouldn’t be either. Sycamore calculated the distribution of outputs with an approximate accuracy of 0.2%—just enough to distinguish the fingerprint-like spike from the noise in the circuit. So Zhang’s team traded accuracy for speed by cutting some lines in their network and eliminating relevant ports. Losing just eight lines made the calculation 256 times faster while maintaining an accuracy of 0.37%.
The researchers calculated the output model for 1 million of the 9 quadrillion possible number strings, relying on a proprietary innovation to obtain a truly random, representative set. The calculation took 15 hours on 512 GPUs and produced a tellingly spicy result. “It’s fair to say that Google’s experiment was simulated on a conventional computer,” says Dominic Hangleiter, a quantum computer scientist at the University of Maryland, College Park. On a supercomputer, the calculation would take a few tens of seconds, Zhang says — 10 billion times faster than the Google team calculated.
The advance highlights the pitfalls of racing a quantum computer against a conventional one, researchers say. “There is an urgent need for better quantum supremacy experiments,” Aronson says. Zhang suggests a more practical approach: “We need to find some real-world applications to demonstrate the quantum advantage.”
Still, Google’s demo wasn’t just an advertisement, researchers say. Sycamore requires far fewer operations and less power than a supercomputer, Zhang notes. And if Sycamore had a slightly higher accuracy, he says, his team’s simulation couldn’t keep up. As Hangleiter says, “Google’s experiment did what it needed to do, start this race.”
Add Comment