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Quantum computers outperform classical ones, with results you can trust
Three approaches to the issue of quantum results that can’t be verified classically.
There are many algorithms for which it has been mathematically proven that a quantum computer can generate results that would take a classical computer an unreasonable amount of time to generate. Unfortunately, today’s quantum computers either can’t run those algorithms or can only run simplified versions that classical computers can also handle. This has left the field facing a challenging question: Can we demonstrate the promise of quantum computers on today’s noisy, limited hardware?
That’s a more difficult question than it may first appear. If you generate a result that’s out of reach of today’s regular computers, it may not be possible to verify that you got the right result. And given that today’s quantum computers are somewhat error-prone, getting the wrong result is a distinct possibility. Further, in the absence of a mathematical proof of the capabilities of quantum hardware, it’s possible that a better classical algorithm could outperform the quantum hardware.
These issues inspired IBM to launch a quantum advantage tracker. On Thursday, the company announced three new entries that it says clearly show a quantum advantage, each using a different approach to overcoming errors and validating quantum results. “Trusted computing when you can do classical simulations is irrelevant,” IBM’s Jay Gambetta told Ars. “Trusted computing when you can’t do classical simulations is a big deal.”
None of the results are immediately useful, but they hint that we might be heading in the right direction.
At this point, there have been many claims of quantum advantage, and at least one has the potential to be useful. But in a number of high-profile cases, algorithm developers have developed optimized algorithms that have severely reduced the advantage, bringing classical computers back up to par. Another issue is verification. If your quantum computer is generating a statistical pattern by re-running variations on a single set of operations, a systemic error could potentially bias the output, and you couldn’t use a classical computer to check.
These issues are typically handled by performing simplified calculations using fewer qubits and verifying the results on classical hardware. If that works, it’s assumed that the algorithm will continue to work when it is run with more qubits. But that’s not the only option. Some algorithms could produce results that are difficult to calculate but easy to verify—for example, factoring the product of multiplying two large primes.
Unfortunately, if anyone has identified a calculation that could be run on today’s hardware, I’m not aware of it. Computer scientists, therefore, have had to get creative. And really, that’s what today’s announcement is about: three creative ways to handle the fact that today’s processors are error-prone.
One of the new efforts was a collaboration among IBM, RIKEN in Japan, and a small company called Qedma, which develops software that helps mitigate errors in current quantum processors. The work focuses on modeling a Floquet process, in which a system oscillates while subjected to an external force that gradually alters its behavior. Think of a pendulum that gradually slows down due to friction.
These sorts of processes can also occur in quantum systems, and the Qedma team modeled something called an Ising model, which you can think of as a hypothetical two-dimensional grid of magnets, where the orientation of each can affect its neighbors. After setup, the orientations will gradually undergo periodic flips as they try to find a low-energy configuration in which neighboring magnets have opposite orientations. The complexity of modeling the intervening states of the system during these flips increases as you add more magnets.