Quantum computers of the longer term may ultimately outperform their classical counterparts to unravel intractable problems in computer science, medicine, business, chemistry, physics, and other fields. However the machines should not there yet: They’re riddled with inherent errors, which researchers are actively working to scale back. One strategy to study these errors is to make use of classical computers to simulate the quantum systems and confirm their accuracy. The one catch is that as quantum machines grow to be increasingly complex, running simulations of them on traditional computers would take years or longer.
Now, Caltech researchers have invented a brand new method by which classical computers can measure the error rates of quantum machines without having to completely simulate them. The team describes the strategy in a paper within the journal Nature.
“In an ideal world, we would like to scale back these errors. That is the dream of our field,” says Adam Shaw, lead creator of the study and a graduate student who works within the laboratory of Manuel Endres, professor of physics at Caltech. “But within the meantime, we want to higher understand the errors facing our system, so we are able to work to mitigate them. That motivated us to provide you with a brand new approach for estimating the success of our system.”
In the brand new study, the team performed experiments using a variety of easy quantum computer generally known as a quantum simulator. Quantum simulators are more limited in scope than current rudimentary quantum computers and are tailored for specific tasks. The group’s simulator is made up of individually controlled Rydberg atoms — atoms in highly excited states — which they manipulate using lasers.
One key feature of the simulator, and of all quantum computers, is entanglement — a phenomenon during which certain atoms grow to be connected to one another without actually touching. When quantum computers work on an issue, entanglement is of course built up within the system, invisibly connecting the atoms. Last yr, Endres, Shaw, and colleagues revealed that as entanglement grows, those connections opened up in a chaotic or random fashion, meaning that small perturbations result in big changes in the identical way that a butterfly’s flapping wings could theoretically affect global weather patterns.
This increasing complexity is believed to be what gives quantum computers the facility to unravel certain forms of problems much faster than classical computers, similar to those in cryptography during which large numbers should be quickly factored.
But once the machines reach a certain variety of connected atoms, or qubits, they’ll not be simulated using classical computers. “If you get past 30 qubits, things get crazy,” Shaw says. “The more qubits and entanglement you may have, the more complex the calculations are.”
The quantum simulator in the brand new study has 60 qubits, which Shaw says puts it in a regime that’s not possible to simulate exactly. “It becomes a catch-22. We would like to check a regime that is tough for classical computers to work in, but still depend on those classical computers to inform if our quantum simulator is correct.” To satisfy the challenge, Shaw and colleagues took a brand new approach, running classical computer simulations that allow for various amounts of entanglement. Shaw likens this to painting with brushes of various size.
“As an instance our quantum computer is painting the Mona Lisa as an analogy,” he says. “The quantum computer can paint very efficiently and, in theory, perfectly, but it surely makes errors that smear out the paint in parts of the painting. It’s just like the quantum computer has shaky hands. To quantify these errors, we would like our classical computer to simulate what the quantum computer has done, but our Mona Lisa could be too complex for it. It’s as if the classical computers only have giant brushes or rollers and might’t capture the finer details.
“As a substitute, we’ve many classical computers paint the identical thing with progressively finer and finer brushes, after which we squint our eyes and estimate what it will have looked like in the event that they were perfect. Then we use that to check against the quantum computer and estimate its errors. With many cross-checks, we were capable of show this ‘squinting’ is mathematically sound and offers the reply quite accurately.”
The researchers estimated that their 60-qubit quantum simulator operates with an error rate of 91 percent (or an accuracy rate of 9 percent). That will sound low, but it surely is, in actual fact, relatively high for the state of the sector. For reference, the 2019 Google experiment, during which the team claimed their quantum computer outperformed classical computers, had an accuracy of 0.3 percent (though it was a distinct variety of system than the one on this study).
Shaw says: “We now have a benchmark for analyzing the errors in quantum computing systems. That signifies that as we make improvements to the hardware, we are able to measure how well the improvements worked. Plus, with this recent benchmark, we may measure how much entanglement is involved in a quantum simulation, one other metric of its success.”
The Nature paper titled “Benchmarking highly entangled states on a 60-atom analog quantum simulator” was funded by the National Science Foundation (partially via Caltech’s Institute for Quantum Information and Matter, or IQIM), the Defense Advanced Research Projects Agency (DARPA), the Army Research Office, the U.S. Department of Energy’s Quantum Systems Accelerator, the Troesh postdoctoral fellowship, the German National Academy of Sciences Leopoldina, and Caltech’s Walter Burke Institute for Theoretical Physics. Other Caltech authors include former postdocs Joonhee Choi and Pascal Scholl; Ran Finkelstein, Troesh Postdoctoral Scholar Research Associate in Physics; and Andreas Elben, Sherman Fairchild Postdoctoral Scholar Research Associate in Theoretical Physics. Zhuo Chen, Daniel Mark, and Soonwon Choi (BS ’12) of MIT are also authors.
