On Monday, Nature released a paper from Google's quantum computing team that provides a key demonstration of the potential of quantum error correction. Thanks to an improved processor, Google's team found that increasing the number of hardware qubits dedicated to an error-corrected logical qubit led to an exponential increase in performance. By the time the entire 105-qubit processor was dedicated to hosting a single error-corrected qubit, the system was stable for an average of an hour.

In fact, Google told Ars that errors on this single logical qubit were rare enough that it was difficult to study them. The work provides a significant validation that quantum error correction is likely to be capable of supporting the execution of complex algorithms that might require hours to execute.

A new fab

Google is making a number of announcements in association with the paper's release (an earlier version of the paper has been up on the arXiv since August). One of those is that the company is committed enough to its quantum computing efforts that it has built its own fabrication facility for its superconducting processors.

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By some measures, AI systems are now competitive with traditional computing methods for generating weather forecasts. Because their training penalizes errors, however, the forecasts tend to get "blurry"—as you move further ahead in time, the models make fewer specific predictions since those are more likely to be wrong. As a result, you start to see things like storm tracks broadening and the storms themselves losing clearly defined edges.

But using AI is still extremely tempting because the alternative is a computational atmospheric circulation model, which is extremely compute-intensive. Still, it's highly successful, with the ensemble model from the European Centre for Medium-Range Weather Forecasts considered the best in class.

In a paper being released today, Google's DeepMind claims its new AI system manages to outperform the European model on forecasts out to at least a week and often beyond. DeepMind's system, called GenCast, merges some computational approaches used by atmospheric scientists with a diffusion model, commonly used in generative AI. The result is a system that maintains high resolution while cutting the computational cost significantly.

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One year ago, I didn’t know how to bake bread. I just knew how to follow a recipe.

If everything went perfectly, I could turn out something plain but palatable. But should anything change—temperature, timing, flour, Mercury being in Scorpio—I’d turn out a partly poofy pancake. I presented my partly poofy pancakes to people, and they were polite, but those platters were not particularly palatable.

During a group vacation last year, a friend made fresh sourdough loaves every day, and we devoured it. He gladly shared his knowledge, his starter, and his go-to recipe. I took it home, tried it out, and made a naturally leavened, artisanal pancake.

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We're nearing the end of the year, and there are typically a flood of announcements regarding quantum computers around now, in part because some companies want to live up to promised schedules. Most of these involve evolutionary improvements on previous generations of hardware. But this year, we have something new: the first company to market with a new qubit technology.

The technology is called a dual-rail qubit, and it is intended to make the most common form of error trivially easy to detect in hardware, thus making error correction far more efficient. And, while tech giant Amazon has been experimenting with them, a startup called Quantum Circuits is the first to give the public access to dual-rail qubits via a cloud service.

While the tech is interesting on its own, it also provides us with a window into how the field as a whole is thinking about getting error-corrected quantum computing to work.

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In September, Microsoft made an unusual combination of announcements. It demonstrated progress with quantum error correction, something that will be needed for the technology to move much beyond the interesting demo phase, using hardware from a quantum computing startup called Quantinuum. At the same time, however, the company also announced that it was forming a partnership with a different startup, Atom Computing, which uses a different technology to make qubits available for computations.

Given that, it was probably inevitable that the folks in Redmond, Washington, would want to show that similar error correction techniques would also work with Atom Computing's hardware. It didn't take long, as the two companies are releasing a draft manuscript describing their work on error correction today. The paper serves as both a good summary of where things currently stand in the world of error correction, as well as a good look at some of the distinct features of computation using neutral atoms.

Atoms and errors

While we have various technologies that provide a way of storing and manipulating bits of quantum information, none of them can be operated error-free. At present, errors make it difficult to perform even the simplest computations that are clearly beyond the capabilities of classical computers. More sophisticated algorithms would inevitably encounter an error before they could be completed, a situation that would remain true even if we could somehow improve the hardware error rates of qubits by a factor of 1,000—something we're unlikely to ever be able to do.

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