Recently, Congress' Select Subcommittee on the Coronavirus Pandemic released its final report. The basic gist is about what you'd expect from a Republican-run committee, in that it trashes a lot of Biden-era policies and state-level responses while praising a number of Trump's decisions. But what's perhaps most striking is how it tackles a variety of scientific topics, including many where there's a large, complicated body of evidence.

Notably, this includes conclusions about the origin of the pandemic, which the report describes as "most likely" emerging from a lab rather than being the product of the zoonotic transfer between an animal species and humans. The latter explanation is favored by many scientists.

The conclusions themselves aren't especially interesting; they're expected from a report with partisan aims. But the method used to reach those conclusions is often striking: The Republican majority engages in a process of systematically changing the standard of evidence needed for it to reach a conclusion. For a conclusion the report's authors favor, they'll happily accept evidence from computer models or arguments from an editorial in the popular press; for conclusions they disfavor, they demand double-blind controlled clinical trials.

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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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On Friday, the US Department of Agriculture (USDA) announced that it would begin a nationwide testing program for the presence of the H5N1 flu virus, also known as the bird flu. Testing will focus on pre-pasteurized milk at dairy processing facilities (pasteurization inactivates the virus), but the order that's launching the program will require anybody involved with milk production before then to provide samples to the USDA on request. That includes "any entity responsible for a dairy farm, bulk milk transporter, bulk milk transfer station, or dairy processing facility."

The ultimate goal is to identify individual herds where the virus is circulating and use the agency's existing powers to do contact tracing and restrict the movement of cattle, with the ultimate goal of eliminating the virus from US herds.

A bovine disease vector

At the time of publication, the CDC had identified 58 cases of humans infected by the H5N1 flu virus, over half of them in California. All but two have come about due to contact with agriculture, either cattle (35 cases) or poultry (21). The virus's genetic material has appeared in the milk supply and, although pasteurization should eliminate any intact infectious virus, raw milk is notable for not undergoing pasteurization, which has led to at least one recall when the virus made its way into raw milk. And we know the virus can spread to other species if they drink milk from infected cows.

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2023 was always going to be a hot year, given that warmer El Niño conditions were superimposed on the long-term trend of climate change driven by our greenhouse gas emissions. But it's not clear anybody was expecting the striking string of hot months that allowed the year to easily eclipse any previous year on record. As the warmth has continued at record levels even after the El Niño faded, it's an event that seems to demand an explanation.

On Thursday, a group of German scientists—Helge Goessling, Thomas Rackow, and Thomas Jung—released a paper that attempts to provide one. They present data that suggests the Earth is absorbing more incoming sunlight than it has in the past, largely due to reduced cloud cover.

Balancing the numbers on radiation

Years with strong El Niño conditions tend to break records. But the 2023 El Niño was relatively mild. The effects of the phenomenon are also directly felt in the tropical Pacific, yet ocean temperatures set records in the Atlantic and contributed to a massive retreat in ice near Antarctica. So, there are clearly limits to what can be attributed to El Niño. Other influences that have been considered include the injection of water vapor into the stratosphere by the Hunga Tonga eruption, and a reduction in sulfur emissions due to new rules governing international shipping. 2023 also corresponds to a peak in the most recent solar cycle.

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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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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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