Physics

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New quantum hardware puts the mechanics in quantum mechanics

Computer science, Helios, Physics, quantinuum, quantum computing, quantum mechanics, Science, trapped ion / /

As a test case, the machine was used to test a model of superconductivity.

Quantum computers based on ions or atoms have one major advantage: The hardware itself isn’t manufactured, so there’s no device-to-device variability. Every atom is the same and should perform similarly every time. And since the qubits themselves can be moved around, it’s theoretically possible to entangle any atom or ion with any other in the system, allowing for a lot of flexibility in how algorithms and error correction are performed.

This combination of consistent, high-fidelity performance with all-to-all connectivity has led many key demonstrations of quantum computing to be done on trapped-ion hardware. Unfortunately, the hardware has been held back a bit by relatively low qubit counts—a few dozen compared to the hundred or more seen in other technologies. But on Wednesday, a company called Quantinuum announced a new version of its trapped-ion hardware that significantly boosts the qubit count and uses some interesting technology to manage their operation.

Trapped-ion computing

Both neutral atom and trapped-ion computers store their qubits in the spin of the nucleus. That spin is somewhat shielded from the environment by the cloud of electrons around the nucleus, giving these qubits a relatively long coherence time. While neutral atoms are held in place by a network of lasers, trapped ions are manipulated via electromagnetic control based on the ion’s charge. This means that key components of the hardware can be built using standard electronic manufacturing, although lasers are still needed for manipulations and readout.

While the electronics are static—they stay wherever they were manufactured—they can be used to move the ions around. That means that as long as the trackways the atoms can move on enable it, any two ions can be brought into close proximity and entangled. This all-to-all connectivity can enable more efficient implementation of algorithms performed directly on the hardware qubits or the use of error-correction codes that require a complicated geometry of connections. That’s one reason why Microsoft used a Quantinuum machine to demonstrate error-correction code based on a tesseract.

But arranging the trackways so that any two qubits can be next to each other can become increasingly complicated. Moving ions around is a relatively slow process, so retrieving two ions from the far ends of a chip too often can cause a system to start pushing up against the coherence time of the qubits. In the long term, Quantinuum plans to build chips with a square grid reminiscent of the street layout of many cities. But doing so will require a mastery of controlling the flow of ions through four-way intersections.

And that’s what Quantinuum is doing in part with its new chip, named Helios. It has a single intersection that couples two ion-storage areas, enabling operations as ions slosh from one end of the chip to the other. And it comes with significantly more qubits than its earlier hardware, moving from 56 to 96 qubits without sacrificing performance. “We’ve kept and actually even improved the two qubit gate fidelity,” Quantinuum VP Jenni Strabley told Ars. “So we’re not seeing any degradation in the two-qubit gate fidelity as we go to larger and larger sizes.”

Doing the loop

The image below is taken using the fluorescence of the atoms in the hardware itself. As you can see, the layout is dominated by two features: A loop at the left and two legs extending to the right. They’re connected by a four-way intersection. The Quantinuum staff described this intersection as being central to the computer’s operation.

A black background on which a series of small blue dots trace out a circle and two parallel lines connected by an x-shaped junction.

The actual ions trace out the physical layout of the Helios system, featuring a storage ring and two legs that contain dedicated operation sites. Credit: Quantinuum

The system works by rotating the ions around the loop. As an ion reaches the intersection, the system chooses whether to kick it into one of the legs and, if so, which leg. “We spin that ring almost like a hard drive, really, and whenever the ion that we want to gate gets close to the junction, there’s a decision that happens: Either that ion goes [into the legs], or it kind of makes a little turn and goes back into the ring,” said David Hayes, Quantinuum’s director of Computational Design and Theory. “And you can make that decision with just a few electrodes that are right at that X there.”

Each leg has a region where operations can take place, so this system can ensure that the right qubits are present together in the operation zones for things like two-qubit gates. Once the operations are complete, the qubits can be moved into the leg storage regions, and new qubits can be shuffled in. When the legs fill up, the qubits can be sent back to the loop, and the process is restarted.

“You get less traffic jams if all the traffic is running one way going through the gate zones,” Hayes told Ars. “If you had to move them past each other, you would have to do kind of physical swaps, and you want to avoid that.”

Obviously, issuing all the commands to control the hardware will be quite challenging for anything but the simplest operations. That puts an increasing emphasis on the compilers that add a significant layer of abstraction between what you want a quantum computer to do and the actual hardware commands needed to implement it. Quantinuum has developed its own compiler to take user-generated code and produce something that the control system can convert into the sequence of commands needed.

The control system now incorporates a real-time engine that can read data from Helios and update the commands it issues based on the state of the qubits. Quantinuum has this portion of the system running on GPUs rather than requiring customized hardware.

Quantinuum’s SDK for users is called Guppy and is based on Python, which has been modified to allow users to describe what they’d like the system to do. Helios is being accompanied by a new version of Guppy that includes some traditional programming tools like FOR loops and IF-based conditionals. These will be critical for the sorts of things we want to do as we move toward error-corrected qubits. This includes testing for errors, fixing them if they’re present, or repeatedly attempting initialization until it succeeds without error.

Hayes said the new version is also moving toward error correction. Thanks to Guppy’s ability to dynamically reassign qubits, Helios will be able to operate as a machine with 94 qubits while detecting errors on any of them. Alternatively, the 96 hardware qubits can be configured as a single unit that hosts 48 error-corrected qubits. “It’s actually a concatenated code,” Hayes told Ars. “You take two error detection codes and weave them together… it’s a single code block, but it has 48 logical cubits housed inside of it.” (Hayes said it’s a distance-four code, meaning it can fix up to two errors that occur simultaneously.)

Tackling superconductivity

While Quantinuum hardware has always had low error rates relative to most of its competitors, there was only so much you could do with 56 qubits. With 96 now at their disposal, researchers at the company decided to build a quantum implementation of a model (called the Fermi-Hubbard model) that’s meant to help study the electron pairing that takes place during the transition to superconductivity.

“There are definitely terms that the model doesn’t capture,” Quantinuum’s Henrik Dreyer acknowledged. “They neglect their electrorepulsion that [the electrons] still have—I mean, they’re still negatively charged; they are still repelling. There are definitely terms that the model doesn’t capture. On the other hand, I should say that this Fermi-Hubbard model—it has many of the features that a superconductor has.”

Superconductivity occurs when electrons join to form what are called Cooper pairs, overcoming their normal repulsion. And the model can tell that apart from normal conductivity in the same material.

“You ask the question ‘What’s the chance that one of the charged particles spontaneously disappears because of quantum fluctuations and goes over here?’” Dreyer said, describing what happens when simulating a conductor. “What people do in superconductivity is they take this concept, but instead of asking what’s the chance of a single-charge particle to tunnel over there spontaneously, they’re asking what is the chance of a pair to tunnel spontaneously?”

Even in its simplified form, however, it’s still a model of a quantum system, with all the computational complexity that comes with that. So the Quantinuum team modeled a few systems that classical computers struggle with. One was simply looking at a larger grid of atoms than most classical simulations have done; another expanded the grid in an additional dimension, modeling layers of a material. Perhaps the most complicated simulation involved what happens when a laser pulse of the right wavelength hits a superconductor at room temperature, an event that briefly induces a superconducting state.

And the system produced results, even without error correction. “It’s maybe a technical point, but I think it’s very important technical point, which is [that] the circuits that we ran, they all had errors,” Dreyer told Ars. “Maybe on the average of three or so errors, and for some reason, that is not very fully understood for this application, it doesn’t matter. You still get almost the perfect result in some of these cases.”

That said, he also indicated that having higher-fidelity hardware would help the team do a better job of putting the system in a ground state or running the simulation for longer. But those will have to wait for future hardware.

What’s next

If you look at Quantinuum’s roadmap for that future hardware, Helios would appear to be the last of its kind. It and earlier versions of the processors have loops and large straight stretches; everything in the future features a grid of squares. But both Strabley and Hayes said that Helios has several key transitional features. “Those ions are moving through that junction many, many times over the course of a circuit,” Strabley told Ars. “And so it’s really enabled us to work on the reliability of the junction, and that will translate into the large-scale systems.”

Image of a product roadmap, with years from 2020 to 2029 noted across the top. There are five processors arrayed from left to right, each with increasingly complex geometry.

Helios sits at the pivot between the simple geometries of earlier Quantinuum processors and the grids of future designs. Credit: Quantinuum

The collection of squares seen in future processors will also allow the same sorts of operations to be done with the loop-and-legs of Helios. Some squares can serve as the equivalent of a loop in terms of storage and sorting, while some of the straight lines nearby can be used for operations.

“What will be common to both of them is kind of the general concept that you can have a storage and sorting region and then gating regions on the side and they’re separated from one another,” Hayes said. “It’s not public yet, but that’s the direction we’re heading: a storage region where you can do really fast sorting in these 2D grids, and then gating regions that have parallelizable logical operations.”

In the meantime, we’re likely to see improvements made to Helios—ideas that didn’t quite make today’s release. “There’s always one more improvement that people want to make, and I’m the person that says, ‘No, we’re going to go now. Put this on the market, and people are going to go use it,’” Strabley said. “So there is a long list of things that we’re going to add to improve the performance. So expect that over the course of Helios, the performance is going to get better and better and better.”

That performance is likely to be used for the sort of initial work done on superconductivity or the algorithm recently described by Google, which is at or a bit beyond what classical computers can manage and may start providing some useful insights. But it will still be a generation or two before we start seeing quantum computing fulfill some of its promise.

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John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.

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Google has a useful quantum algorithm that outperforms a supercomputer

Computer science, Features, Google, NMR, Physics, quantum computing, quantum mechanics, Science / /

An approach it calls “quantum echoes” takes 13,000 times longer on a supercomputer.

Image of a silvery plate labeled with

The work relied on Google’s current-generation quantum hardware, the Willow chip. Credit: Google

The work relied on Google’s current-generation quantum hardware, the Willow chip. Credit: Google

A few years back, Google made waves when it claimed that some of its hardware had achieved quantum supremacy, performing operations that would be effectively impossible to simulate on a classical computer. That claim didn’t hold up especially well, as mathematicians later developed methods to help classical computers catch up, leading the company to repeat the work on an improved processor.

While this back-and-forth was unfolding, the field became less focused on quantum supremacy and more on two additional measures of success. The first is quantum utility, in which a quantum computer performs computations that are useful in some practical way. The second is quantum advantage, in which a quantum system completes calculations in a fraction of the time it would take a typical computer. (IBM and a startup called Pasqual have published a useful discussion about what would be required to verifiably demonstrate a quantum advantage.)

Today, Google and a large collection of academic collaborators are publishing a paper describing a computational approach that demonstrates a quantum advantage compared to current algorithms—and may actually help us achieve something useful.

Out of time

Google’s latest effort centers on something it’s calling “quantum echoes.” The approach could be described as a series of operations on the hardware qubits that make up its machine. These qubits hold a single bit of quantum information in a superposition between two values, with probabilities of finding the qubit in one value or the other when it’s measured. Each qubit is entangled with its neighbors, allowing its probability to influence those of all the qubits around it. The operations that allow computation, called gates, are ways of manipulating these probabilities. Most current hardware, including Google’s, perform manipulations on one or two qubits at a time (termed one- and two-qubit gates, respectively.

For quantum echoes, the operations involved performing a set of two-qubit gates, altering the state of the system, and later performing the reverse set of gates. On its own, this would return the system to its original state. But for quantum echoes, Google inserts single-qubit gates performed with a randomized parameter. This alters the state of the system before the reverse operations take place, ensuring that the system won’t return to exactly where it started. That explains the “echoes” portion of the name: You’re sending an imperfect copy back toward where things began, much like an echo involves the imperfect reversal of sound waves.

That’s what the process looks like in terms of operations performed on the quantum hardware. But it’s probably more informative to think of it in terms of a quantum system’s behavior. As Google’s Tim O’Brien explained, “You evolve the system forward in time, then you apply a small butterfly perturbation, and then you evolve the system backward in time.” The forward evolution is the first set of two qubit gates, the small perturbation is the randomized one qubit gate, and the second set of two qubit gates is the equivalent of sending the system backward in time.

Because this is a quantum system, however, strange things happen. “On a quantum computer, these forward and backward evolutions, they interfere with each other,” O’Brien said. One way to think about that interference is in terms of probabilities. The system has multiple paths between its start point and the point of reflection—where it goes from evolving forward in time to evolving backward—and from that reflection point back to a final state. Each of those paths has a probability associated with it. And since we’re talking about quantum mechanics, those paths can interfere with each other, increasing some probabilities at the expense of others. That interference ultimately determines where the system ends up.

(Technically, these are termed “out of time order correlations,” or OTOCs. If you read the Nature paper describing this work, prepare to see that term a lot.)

Demonstrating advantage

So how do you turn quantum echoes into an algorithm? On its own, a single “echo” can’t tell you much about the system—the probabilities ensure that any two runs might show different behaviors. But if you repeat the operations multiple times, you can begin to understand the details of this quantum interference. And performing the operations on a quantum computer ensures that it’s easy to simply rerun the operations with different random one-qubit gates and get many instances of the initial and final states—and thus a sense of the probability distributions involved.

This is also where Google’s quantum advantage comes from. Everyone involved agrees that the precise behavior of a quantum echo of moderate complexity can be modeled using any leading supercomputer. But doing so is very time-consuming, so repeating those simulations a few times becomes unrealistic. The paper estimates that a measurement that took its quantum computer 2.1 hours to perform would take the Frontier supercomputer approximately 3.2 years. Unless someone devises a far better classical algorithm than what we have today, this represents a pretty solid quantum advantage.

But is it a useful algorithm? The repeated sampling can act a bit like the Monte Carlo sampling done to explore the behavior of a wide variety of physical systems. Typically, however, we don’t view algorithms as modeling the behavior of the underlying hardware they’re being run on; instead, they’re meant to model some other physical system we’re interested in. That’s where Google’s announcement stands apart from its earlier work—the company believes it has identified an interesting real-world physical system with behaviors that the quantum echoes can help us understand.

That system is a small molecule in a Nuclear Magnetic Resonance (NMR) machine. In a second draft paper being published on the arXiv later today, Google has collaborated with a large collection of NMR experts to explore that use.

From computers to molecules

NMR is based on the fact that the nucleus of every atom has a quantum property called spin. When nuclei are held near to each other, such as when they’re in the same molecule, these spins can influence one another. NMR uses magnetic fields and photons to manipulate these spins and can be used to infer structural details, like how far apart two given atoms are. But as molecules get larger, these spin networks can extend for greater distances and become increasingly complicated to model. So NMR has been limited to focusing on the interactions of relatively nearby spins.

For this work, though, the researchers figured out how to use an NMR machine to create the physical equivalent of a quantum echo in a molecule. The work involved synthesizing the molecule with a specific isotope of carbon (carbon-13) in a known location in the molecule. That isotope could be used as the source of a signal that propagates through the network of spins formed by the molecule’s atoms.

“The OTOC experiment is based on a many-body echo, in which polarization initially localized on a target spin migrates through the spin network, before a Hamiltonian-engineered time-reversal refocuses to the initial state,” the team wrote. “This refocusing is sensitive to perturbations on distant butterfly spins, which allows one to measure the extent of polarization propagation through the spin network.”

Naturally, something this complicated needed a catchy nickname. The team came up with TARDIS, or Time-Accurate Reversal of Dipolar InteractionS. While that name captures the “out of time order” aspect of OTOC, it’s simply a set of control pulses sent to the NMR sample that starts a perturbation of the molecule’s network of nuclear spins. A second set of pulses then reflects an echo back to the source.

The reflections that return are imperfect, with noise coming from two sources. The first is simply imperfections in the control sequence, a limitation of the NMR hardware. But the second is the influence of fluctuations happening in distant atoms along the spin network. These happen at a certain frequency at random, or the researchers could insert a fluctuation by targeting a specific part of the molecule with randomized control signals.

The influence of what’s going on in these distant spins could allow us to use quantum echoes to tease out structural information at greater distances than we currently do with NMR. But to do so, we need an accurate model of how the echoes will propagate through the molecule. And again, that’s difficult to do with classical computations. But it’s very much within the capabilities of quantum computing, which the paper demonstrates.

Where things stand

For now, the team stuck to demonstrations on very simple molecules, making this work mostly a proof of concept. But the researchers are optimistic that there are many ways the system could be used to extract structural information from molecules at distances that are currently unobtainable using NMR. They list a lot of potential upsides that should be explored in the discussion of the paper, and there are plenty of smart people who would love to find new ways of using their NMR machines, so the field is likely to figure out pretty quickly which of these approaches turns out to be practically useful.

The fact that the demonstrations were done with small molecules, however, means that the modeling run on the quantum computer could also have been done on classical hardware (it only required 15 hardware qubits). So Google is claiming both quantum advantage and quantum utility, but not at the same time. The sorts of complex, long-distance interactions that would be out of range of classical simulation are still a bit beyond the reach of the current quantum hardware. O’Brien estimated that the hardware’s fidelity would have to improve by a factor of three or four to model molecules that are beyond classical simulation.

The quantum advantage issue should also be seen as a work in progress. Google has collaborated with enough researchers at enough institutions that there’s unlikely to be a major improvement in algorithms that could allow classical computers to catch up. Until the community as a whole has some time to digest the announcement, though, we shouldn’t take that as a given.

The other issue is verifiability. Some quantum algorithms will produce results that can be easily verified on classical hardware—situations where it’s hard to calculate the right result but easy to confirm a correct answer. Quantum echoes isn’t one of those, so we’ll need another quantum computer to verify the behavior Google has described.

But Google told Ars nothing is up to the task yet. “No other quantum processor currently matches both the error rates and number of qubits of our system, so our quantum computer is the only one capable of doing this at present,” the company said. (For context, Google says that the algorithm was run on up to 65 qubits, but the chip has 105 qubits total.)

There’s a good chance that other companies would disagree with that contention, but it hasn’t been possible to ask them ahead of the paper’s release.

In any case, even if this claim proves controversial, Google’s Michel Devoret, a recent Nobel winner, hinted that we shouldn’t have long to wait for additional ones. “We have other algorithms in the pipeline, so we will hopefully see other interesting quantum algorithms,” Devoret said.

Nature, 2025. DOI: 10.1038/s41586-025-09526-6  (About DOIs).

Photo of John Timmer

John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.

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2025 Nobel Prize in Physics awarded for macroscale quantum tunneling

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John Clarke, Michel H. Devoret, and John Martinis built an electrical circuit-based oscillator on a microchip.

A device consisting of four transmon qubits, four quantum buses, and four readout resonators fabricated by IBM in 2017. Credit: ay M. Gambetta, Jerry M. Chow & Matthias Steffen/CC BY 4.0

The 2025 Nobel Prize in Physics has been awarded to John Clarke, Michel H. Devoret, and John M. Martinis “for the discovery of macroscopic quantum tunneling and energy quantization in an electrical circuit.” The Nobel committee said during a media briefing that the laureates’ work provides opportunities to develop “the next generation of quantum technology, including quantum cryptography, quantum computers, and quantum sensors.” The three men will split the $1.1 million (11 million Swedish kroner) prize money. The presentation ceremony will take place in Stockholm on December 10, 2025.

“To put it mildly, it was the surprise of my life,” Clarke told reporters by phone during this morning’s press conference. “Our discovery in some ways is the basis of quantum computing. Exactly at this moment where this fits in is not entirely clear to me. One of the underlying reasons that cellphones work is because of all this work.”

When physicists began delving into the strange new realm of subatomic particles in the early 20th century, they discovered a realm where the old, deterministic laws of classical physics no longer apply. Instead, uncertainty reigns supreme. It is a world governed not by absolutes, but by probabilities, where events that would seem impossible on the macroscale occur on a regular basis.

For instance, subatomic particles can “tunnel” through seemingly impenetrable energy barriers. Imagine that an electron is a water wave trying to surmount a tall barrier. Unlike water, if the electron’s wave is shorter than the barrier, there is still a small probability that it will seep through to the other side.

This neat little trick has been experimentally verified many times. In the 1950s, physicists devised a system in which the flow of electrons would hit an energy barrier and stop because they lacked sufficient energy to surmount that obstacle. But some electrons didn’t follow the established rules of behavior. They simply tunneled right through the energy barrier.

(l-r): John Clarke, Michel H. Devoret and John M. Martinis

(l-r): John Clarke, Michel H. Devoret, and John M. Martinis. Credit: Niklas Elmehed/Nobel Prize Outreach

From subatomic to the macroscale

Clarke, Devoret, and Martinis were the first to demonstrate that quantum effects, such as quantum tunneling and energy quantization, can operate on macroscopic scales, not just one particle at a time.

After earning his PhD from University of Cambridge, Clarke came to the University of California, Berkeley, as a postdoc, eventually joining the faculty in 1969. By the mid-1980s, Devoret and Martinis had joined Clarke’s lab as a postdoc and graduate student, respectively. The trio decided to look for evidence of macroscopic quantum tunneling using a specialized circuit called a Josephson junction—a macroscopic device that takes advantage of a tunneling effect that is now widely used in quantum computing, quantum sensing, and cryptography.

A Josephson junction—named after British physicist Brian Josephson, who won the 1973 Nobel Prize in physics—is basically two semiconductor pieces separated by an insulating barrier. Despite this small gap between two conductors, electrons can still tunnel through the insulator and create a current. That occurs at sufficiently low temperatures, when the junction becomes superconducting as electrons form so-called “Cooper pairs.”

The team built an electrical circuit-based oscillator on a microchip measuring about one centimeter in size—essentially a quantum version of the classic pendulum. Their biggest challenge was figuring out how to reduce the noise in their experimental apparatus. For their experiments, they first fed a weak current into the junction and measured the voltage—initially zero. Then they increased the current and measured how long it took for the system to tunnel out of its enclosed state to produce a voltage.

Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences

They took many measurements and found that the average current increased as the device’s temperature falls, as expected. But at some point, the temperature got so low that the device became superconducting and the average current became independent of the device’s temperature—a telltale signature of macroscopic quantum tunneling.

The team also demonstrated that the Josephson junction exhibited quantized energy levels—meaning the energy of the system was limited to only certain allowed values, just like subatomic particles can gain or lose energy only in fixed, discrete amounts—confirming the quantum nature of the system. Their discovery effectively revolutionized quantum science, since other scientists could now test precise quantum physics on silicon chips, among other applications.

Lasers, superconductors, and superfluid liquids exhibit quantum mechanical effects at the macroscale, but these arise by combining the behavior of microscopic components. Clarke, Devoret, and Martinis were able to create a macroscopic effect—a measurable voltage—from a macroscopic state. Their system contained billions of Cooper pairs filling the entire superconductor on the chip, yet all of them were described by a single wave function. They behave like a large-scale artificial atom.

In fact, their circuit was basically a rudimentary qubit. Martinis showed in a subsequent experiment that such a circuit could be an information-bearing unit, with the lowest energy state and the first step upward functioning as a 0 and a 1, respectively. This paved the way for such advances as the transmon in 2007: a superconducting charge qubit with reduced sensitivity to noise.

“That quantization of the energy levels is the source of all qubits,” said Irfan Siddiqi, chair of UC Berkeley’s Department of Physics and one of Devoret’s former postdocs. “This was the grandfather of qubits. Modern qubit circuits have more knobs and wires and things, but that’s just how to tune the levels, how to couple or entangle them. The basic idea that Josephson circuits could be quantized and were quantum was really shown in this experiment. The fact that you can see the quantum world in an electrical circuit in this very direct way was really the source of the prize.”

So perhaps it is not surprising that Martinis left academia in 2014 to join Google’s quantum computing efforts, helping to build a quantum computer the company claimed had achieved “quantum supremacy” in 2019. Martinis left in 2020 and co-founded a quantum computing startup, Qolab, in 2022. His fellow Nobel laureate, Devoret, now leads Google’s quantum computing division and is also a faculty member at the University of California, Santa Barbara. As for Clarke, he is now a professor emeritus at UC Berkeley.

“These systems bridge the gap between microscopic quantum behavior and macroscopic devices that form the basis for quantum engineering,” Gregory Quiroz, an expert in quantum information science and quantum algorithms at Johns Hopkins University, said in a statement. “The rapid progress in this field over the past few decades—in part fueled by their critical results—has allowed superconducting qubits to go from small-scale laboratory experiments to large-scale, multi-qubit devices capable of realizing quantum computation. While we are still on the hunt for undeniable quantum advantage, we would not be where we are today without many of their key contributions to the field.”

As is often the case with fundamental research, none of the three physicists realized at the time how significant their discovery would be in terms of its impact on quantum computing and other applications.

“This prize really demonstrates what the American system of science has done best,” Jonathan Bagger, CEO of the American Physical Society, told the New York Times. “It really showed the importance of the investment in research for which we do not yet have an application, because we know that sooner or later, there will be an application.”

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Jennifer is a senior writer at Ars Technica with a particular focus on where science meets culture, covering everything from physics and related interdisciplinary topics to her favorite films and TV series. Jennifer lives in Baltimore with her spouse, physicist Sean M. Carroll, and their two cats, Ariel and Caliban.

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Scientists unlock secret to thick, stable beer foams

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For many beer lovers, a nice thick head of foam is one of life’s pure pleasures, and the longer that foam lasts, the better the beer-drinking experience. A team of Swiss researchers spent seven years studying why some beer foams last longer than others and found that the degree of fermentation—i.e., whether a given beer has been singly, doubly, or triply fermented—is crucial, according to a new paper published in the journal Physics of Fluids.

As previously reported, foams are ubiquitous in everyday life, found in foods (whipped cream), beverages (beer, cappuccino), shaving cream and hair-styling mousse, packing peanuts, building insulation, flame-retardant materials, and so forth. All foams are the result of air being beaten into a liquid formula that contains some kind of surfactant (active surface agent), usually fats or proteins in edible foams, or chemical additives in non-edible products. That surfactant strengthens the liquid film walls of the bubbles to keep them from collapsing.

Individual bubbles typically form a sphere because that’s the shape with the minimum surface area for any volume and hence is the most energy-efficient. One reason for the minimizing principle when it comes to a bubble’s shape is that many bubbles can then tightly pack together to form a foam. But bubbles “coarsen” over time, the result of gravity pulling down on the liquid and thinning out the walls. Eventually, they start to look more like soccer balls (polyhedrons). In a coarsening foam, smaller bubbles are gradually absorbed by larger ones. There is less and less liquid to separate the individual bubbles, so they press together to fill the space.

This “jamming” is why foams are typically far more rigid than their gas (95 percent) and liquid (5 percent) components. The more tightly the bubbles jam together, the less they can move around and the greater the pressure inside them becomes, giving them properties of a solid.

Various factors can affect foam stability. For instance, in 2019, Japanese researchers investigated a phenomenon known as “collective bubble collapse,” or CBC, in which breaking one bubble at the edge of a foam results in a cascading effect as the breakage spreads to other bubbles in the foam. They identified two distinct mechanisms for the resulting CBCs: a so-called “propagating mode,” in which a broken bubble is absorbed into the liquid film, and a “penetrating mode,” in which the breakage of a bubble causes droplets to shoot off and hit other bubbles, causing them to break in turn.

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Physics of badminton’s new killer spin serve

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Serious badminton players are constantly exploring different techniques to give them an edge over opponents. One of the latest innovations is the spin serve, a devastatingly effective method in which a player adds a pre-spin just before the racket contacts the shuttlecock (aka the birdie). It’s so effective—some have called it “impossible to return“—that the Badminton World Federation (BWF) banned the spin serve in 2023, at least until after the 2024 Paralympic Games in Paris.

The sanction wasn’t meant to quash innovation but to address players’ concerns about the possible unfair advantages the spin serve conferred. The BWF thought that international tournaments shouldn’t become the test bed for the technique, which is markedly similar to the previously banned “Sidek serve.” The BWF permanently banned the spin serve earlier this year. Chinese physicists have now teased out the complex fundamental physics of the spin serve, publishing their findings in the journal Physics of Fluids.

Shuttlecocks are unique among the various projectiles used in different sports due to their open conical shape. Sixteen overlapping feathers protrude from a rounded cork base that is usually covered in thin leather. The birdies one uses for leisurely backyard play might be synthetic nylon, but serious players prefer actual feathers.

Those overlapping feathers give rise to quite a bit of drag, such that the shuttlecock will rapidly decelerate as it travels and its parabolic trajectory will fall at a steeper angle than its rise. The extra drag also means that players must exert quite a bit of force to hit a shuttlecock the full length of a badminton court. Still, shuttlecocks can achieve top speeds of more than 300 mph. The feathers also give the birdie a slight natural spin around its axis, and this can affect different strokes. For instance, slicing from right to left, rather than vice versa, will produce a better tumbling net shot.

Chronophotographies of shuttlecocks after an impact with a racket

Chronophotographies of shuttlecocks after an impact with a racket. Credit: Caroline Cohen et al., 2015

The cork base makes the birdie aerodynamically stable: No matter how one orients the birdie, once airborne, it will turn so that it is traveling cork-first and will maintain that orientation throughout its trajectory. A 2015 study examined the physics of this trademark flip, recording flips with high-speed video and conducting free-fall experiments in a water tank to study how its geometry affects the behavior. The latter confirmed that shuttlecock feather geometry hits a sweet spot in terms of an opening inclination angle that is neither too small nor too large. And they found that feather shuttlecocks are indeed better than synthetic ones, deforming more when hit to produce a more triangular trajectory.

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ice-discs-slingshot-across-a-metal-surface-all-on-their-own

Ice discs slingshot across a metal surface all on their own

fluid dynamics, Leidenfrost effect, materials science, Physics, reverse Leidenfrost effect, Science / /

VA Tech experiment was inspired by Death Valley’s mysterious “sailing stones” at Racetrack Playa.

Graduate student Jack Tapocik sets up ice on an engineered surface in the VA Tech lab of Jonathan Boreyko. Credit: Alex Parrish/Virginia Tech

Scientists have figured out how to make frozen discs of ice self-propel across a patterned metal surface, according to a new paper published in the journal ACS Applied Materials and Interfaces. It’s the latest breakthrough to come out of the Virginia Tech lab of mechanical engineer Jonathan Boreyko.

A few years ago, Boreyko’s lab experimentally demonstrated a three-phase Leidenfrost effect in water vapor, liquid water, and ice. The Leidenfrost effect is what happens when you dash a few drops of water onto a very hot, sizzling skillet. The drops levitate, sliding around the pan with wild abandon. If the surface is at least 400° Fahrenheit (well above the boiling point of water), cushions of water vapor, or steam, form underneath them, keeping them levitated. The effect also works with other liquids, including oils and alcohol, but the temperature at which it manifests will be different.

Boreyko’s lab discovered that this effect can also be achieved in ice simply by placing a thin, flat disc of ice on a heated aluminum surface. When the plate was heated above 150° C (302° F), the ice did not levitate on a vapor the way liquid water does. Instead, there was a significantly higher threshold of 550° Celsius (1,022° F) for levitation of the ice to occur. Unless that critical threshold is reached, the meltwater below the ice just keeps boiling in direct contact with the surface. Cross that critical point and you will get a three-phase Leidenfrost effect.

The key is a temperature differential in the meltwater just beneath the ice disc. The bottom of the meltwater is boiling, but the top of the meltwater sticks to the ice. It takes a lot to maintain such an extreme difference in temperature, and doing so consumes most of the heat from the aluminum surface, which is why it’s harder to achieve levitation of an ice disc. Ice can suppress the Leidenfrost effect even at very high temperatures (up to 550° C), which means that using ice particles instead of liquid droplets would be better for many applications involving spray quenching: rapid cooling in nuclear power plants, for example, firefighting, or rapid heat quenching when shaping metals.

This time around, Boreyko et al. have turned their attention to what the authors term “a more viscous analog” to a Leidenfrost ratchet, a form of droplet self-propulsion. “What’s different here is we’re no longer trying to levitate or even boil,” Boreyko told Ars. “Now we’re asking a more straightforward question: Is there a way to make ice move across the surface directionally as it is melting? Regular melting at room temperature. We’re not boiling, we’re not levitating, we’re not Leidenfrosting. We just want to know, can we make ice shoot across the surface if we design a surface in the right way?”

Mysterious moving boulders

The researchers were inspired by Death Valley’s famous “sailing stones” on Racetrack Playa. Watermelon-sized boulders are strewn throughout the dry lake bed, and they leave trails in the cracked earth as they slowly migrate a couple of hundred meters each season. Scientists didn’t figure out what was happening until 2014. Although co-author Ralph Lorenz (Johns Hopkins University) admitted he thought theirs would be “the most boring experiment ever” when they first set it up in 2011, two years later, the boulders did indeed begin to move while the playa was covered with a pond of water a few inches deep.

So Lorenz and his co-authors were finally able to identify the mechanism. The ground is too hard to absorb rainfall, and that water freezes when the temperature drops. When temperatures rise above freezing again, the ice starts to melt, creating ice rafts floating on the meltwater. And when the winds are sufficiently strong, they cause the ice rafts to drift along the surface.

A sailing stone in Death Valley's Racetrack Playa.

A sailing stone at Death Valley’s Racetrack Playa. Credit: Tahoenathan/CC BY-SA 3.0

“Nature had to have wind blowing to kind of push the boulder and the ice along the meltwater that was beneath the ice,” said Boreyko. “We thought, what if we could have a similar idea of melting ice moving directionally but use an engineered structure to make it happen spontaneously so we don’t have to have energy or wind or anything active to make it work?”

The team made their ice discs by pouring distilled water into thermally insulated polycarbonate Petrie dishes. This resulted in bottom-up freezing, which minimizes air bubbles in the ice. They then milled asymmetric grooves into uncoated aluminum plates in a herringbone pattern—essentially creating arrowhead-shaped channels—and then bonded them to hot plates heated to the desired temperature. Each ice disc was placed on the plate with rubber tongs, and the experiments were filmed from various angles to fully capture the disc behavior.

The herringbone pattern is the key. “The directionality is what really pushes the water,” Jack Tapocik, a graduate student in Boreyko’s lab, told Ars. “The herringbone doesn’t allow for water to flow backward, the water has to go forward, and that basically pushes the water and the ice together forward. We don’t have a treated surface, so the water just sits on top and the ice all moves as one unit.”

Boreyko draws an analogy to tubing on a river, except it’s the directional channels rather than gravity causing the flow. “You can see [in the video below] how it just follows the meltwater,” he said. “This is your classic entrainment mechanism where if the water flows that way and you’re floating on the water, you’re going to go the same way, too. It’s basically the same idea as what makes a Leidenfrost droplet also move one way: It has a vapor flow underneath. The only difference is that was a liquid drifting on a vapor flow, whereas now we have a solid drifting on a liquid flow. The densities and viscosities are different, but the idea is the same: You have a more dense phase that is drifting on the top of a lighter phase that is flowing directionally.”

Jonathan Boreyko/Virginia Tech

Next, the team repeated the experiment, this time coating the aluminum herringbone surface with water-repellant spray, hoping to speed up the disc propulsion. Instead, they found that the disc ended up sticking to the treated surface for a while before suddenly slingshotting across the metal plate.

“It’s a totally different concept with totally different physics behind it, and it’s so much cooler,” said Tapocik. “As the ice is melting on these coated surfaces, the water just doesn’t want to sit within the channels. It wants to sit on top because of the [hydrophobic] coating we have on there. The ice is directly sticking now to the surface, unlike before when it was floating. You get this elongated puddle in front. The easiest place [for the ice] to be is in the center of this giant, long puddle. So it re-centers, and that’s what moves it forward like a slingshot.”

Essentially, the water keeps expanding asymmetrically, and that difference in shape gives rise to a mismatch in surface tension because the amount of force that surface tension exerts on a body depends on curvature. The flatter puddle shape in front has less curvature than the smaller shape in back. As the video below shows, when the mismatch in surface tension becomes sufficiently strong, “It just rips the ice off the surface and flings it along,” said Boreyko. “In the future, we could try putting little things like magnets on top of the ice. We could probably put a boulder on it if we wanted to. The Death Valley effect would work with or without a boulder because it’s the floating ice raft that moves with the wind.”

Jonathan Boreyko/Virginia Tech

One potential application is energy harvesting. For example, one could pattern the metal surface in a circle rather than a straight line so the melting ice disk would continually rotate. Put magnets on the disk, and they would also rotate and generate power. One might even attach a turbine or gear to the rotating disc.

The effect might also provide a more energy-efficient means of defrosting, a longstanding research interest for Boreyko. “If you had a herringbone surface with a frosting problem, you could melt the frost, even partially, and use these directional flows to slingshot the ice off the surface,” he said. “That’s both faster and uses less energy than having to entirely melt the ice into pure water. We’re looking at potentially over a tenfold reduction in heating requirements if you only have to partially melt the ice.”

That said, “Most practical applications don’t start from knowing the application beforehand,” said Boreyko. “It starts from ‘Oh, that’s a really cool phenomenon. What’s going on here?’ It’s only downstream from that it turns out you can use this for better defrosting of heat exchangers for heat pumps. I just think it’s fun to say that we can make a little melting disk of ice very suddenly slingshot across the table. It’s a neat way to grab your attention and think more about melting and ice and how all this stuff works.”

DOI: ACS Applied Materials and Interfaces, 2025. 10.1021/acsami.5c08993  (About DOIs).

Photo of Jennifer Ouellette

Jennifer is a senior writer at Ars Technica with a particular focus on where science meets culture, covering everything from physics and related interdisciplinary topics to her favorite films and TV series. Jennifer lives in Baltimore with her spouse, physicist Sean M. Carroll, and their two cats, Ariel and Caliban.

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misunderstood-“photophoresis”-effect-could-loft-metal-sheets-to-exosphere

Misunderstood “photophoresis” effect could loft metal sheets to exosphere

engineering, materials science, photophoresis, Physics, Science / /

Photophoresis can generate a tiny bit of lift without any moving parts.

Image of a wooden stand holding a sealed glass bulb with a spinning set of vanes, each of which has a lit and dark side.

Most people would recognize the device in the image above, although they probably wouldn’t know it by its formal name: the Crookes radiometer. As its name implies, placing the radiometer in light produces a measurable change: the blades start spinning.

Unfortunately, many people misunderstand the physics of its operation (which we’ll return to shortly). The actual forces that drive the blades to spin, called photophoresis, can act on a variety of structures as long as they’re placed in a sufficiently low-density atmosphere. Now, a team of researchers has figured out that it may be possible to use the photophoretic effect to loft thin sheets of metal into the upper atmosphere of Earth and other planets. While their idea is to use it to send probes to the portion of the atmosphere that’s too high for balloons and too low for satellites, they have tested some working prototypes a bit closer to the Earth’s surface.

Photophoresis

It’s quite common—and quite wrong—to see explanations of the Crookes radiometer that involve radiation pressure. Supposedly, the dark sides of the blades absorb more photons, each of which carries a tiny bit of momentum, giving the dark side of the blades a consistent push. The problem with this explanation is that photons are bouncing off the silvery side, which imparts even more momentum. If the device were spinning due to radiation pressure, it would be turning in the opposite direction than it actually does.

An excess of the absorbed photons on the dark side is key to understanding how it works, though. Photophoresis operates through the temperature difference that develops between the warm, light-absorbing dark side of the blade and the cooler silvered side.

Any gas molecule that bumps into the dark side will likely pick up some of the excess thermal energy from it and move away from the blade faster than it arrived. At the sorts of atmospheric pressures we normally experience, these molecules don’t get very far before they bump into other gas molecules, which keeps any significant differences from developing.

But a Crookes radiometer is in a sealed glass container with a far lower air pressure. This allows the gas molecules to speed off much farther from the dark surface of the blade before they run into anything, creating an area of somewhat lower pressure at its surface. That causes gas near the surface of the shiny side to rush around and fill this lower-pressure area, imparting the force that starts the blades turning.

It’s pretty impressively inefficient in that sort of configuration, though. So people have spent a lot of time trying to design alternative configurations that can generate a bit more force. One idea with a lot of research traction is a setup that involves two thin metal sheets—one light, one dark—arranged parallel to each other. Both sheets would be heavily perforated to cut down on weight. And a subset of them would have a short pipe connecting holes on the top and bottom sheet. (This has picked up the nickname “nanocardboard.”)

These pipes would serve several purposes. One is to simply link the two sheets into a single unit. Another is to act as an insulator, keeping heat from moving from the dark sheet to the light one, and thus enhancing the temperature gradient. Finally, they provide a direct path for air to move from the top of the light-colored sheet to the bottom of the dark one, giving a bit of directed thrust to help keep the sheets aloft.

Optimization

As you might imagine, there are a lot of free parameters you can tweak: the size of the gap between the sheets, the density of perforations in them, the number of those holes that are connected by a pipe, and so on. So a small team of researchers developed a system to model different configurations and attempt to optimize for lift. (We’ll get to their motivations for doing so a bit later.)

Starting with a disk of nanocardboard, “The inputs to the model are the geometric, optical and thermal properties of the disk, ambient gas conditions, and external radiative heat fluxes on the disk,” as the researchers describe it. “The outputs are the conductive heat fluxes on the two membranes, the membrane temperatures, and the net photophoretic lofting force on the structure.” In general, the ambient gas conditions needed to generate lift are similar to the ones inside the Crookes radiometer: well below the air pressure at sea level.

The model suggested that three trends should influence any final designs. The first is that the density of perforations is a balance. At relatively low elevations (meaning a denser atmosphere), many perforations increase the stress on large sheets, but they decrease the stress for small items at high elevations. The other thing is that, rather than increasing with surface area, lift tends to drop because the sheets are more likely to equilibrate to the prevailing temperatures. A square millimeter of nanocardboard produces over 10 times more lift per surface area than a 10-square-centimeter piece of the same material.

Finally, the researchers calculate that the lift is at its maximum in the mesosphere, the area just above the stratosphere (50–100 kilometers above Earth’s surface).

Light and lifting

The researchers then built a few sheets of nanocardboard to test the output of their model. The actual products, primarily made of chromium, aluminum, and aluminum oxide, were incredibly light, weighing only a gram for a square meter of material. When illuminated by a laser or white LED, they generated measurable force on a testing device, provided the atmosphere was kept sufficiently sparse. With an exposure equivalent to sunlight, the device generated more than it weighed.

It’s a really nice demonstration that we can take a relatively obscure and weak physical effect and design devices that can levitate in the upper atmosphere, powered by nothing more than sunlight—which is pretty cool.

But the researchers have a goal beyond that. The mesophere turns out to be a really difficult part of the atmosphere to study. It’s not dense enough to support balloons or aircraft, but it still has enough gas to make quick work of any satellites. So the researchers really want to turn one of these devices into an instrument-carrying aircraft. Unfortunately, that would mean adding the structural components needed to hold instruments, along with the instruments themselves. And even in the mesosphere, where lift is optimal, these things do not generate much in the way of lift.

Plus, there’s the issue of getting them there, given that they won’t generate enough lift in the lower atmosphere, so they’ll have to be carried into the upper stratosphere by something else and then be released gently enough to not damage their fragile structure. And then, unless you’re lofting them during the polar summer, they will likely come floating back down at night.

None of this is to say this is an impossible dream. But there are definitely a lot of very large hurdles between the work and practical applications on Earth—much less on Mars, where the authors suggest the system could also be used to explore the mesosphere. But even if that doesn’t end up being realistic, this is still a pretty neat bit of physics.

Photo of John Timmer

John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.

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scientists-hid-secret-codes-in-light-to-combat-video-fakes

Scientists hid secret codes in light to combat video fakes

image processing, optics, photometric, Physics, Science, video manipulation / /

Hiding in the light

Previously, the Cornell team had figured out how to make small changes to specific pixels to tell if a video had been manipulated or created by AI. But its success depended on the creator of the video using a specific camera or AI model. Their new method, “noise-coded illumination” (NCI), addresses those and other shortcomings by hiding watermarks in the apparent noise of light sources. A small piece of software can do this for computer screens and certain types of room lighting, while off-the-shelf lamps can be coded via a small attached computer chip.

“Each watermark carries a low-fidelity time-stamped version of the unmanipulated video under slightly different lighting. We call these code videos,” Davis said. “When someone manipulates a video, the manipulated parts start to contradict what we see in these code videos, which lets us see where changes were made. And if someone tries to generate fake video with AI, the resulting code videos just look like random variations.” Because the watermark is designed to look like noise, it’s difficult to detect without knowing the secret code.

The Cornell team tested their method with a broad range of types of manipulation: changing warp cuts, speed and acceleration, for instance, and compositing and deep fakes. Their technique proved robust to things like signal levels below human perception; subject and camera motion; camera flash; human subjects with different skin tones; different levels of video compression; and indoor and outdoor settings.

“Even if an adversary knows the technique is being used and somehow figures out the codes, their job is still a lot harder,” Davis said. “Instead of faking the light for just one video, they have to fake each code video separately, and all those fakes have to agree with each other.” That said, Davis added, “This is an important ongoing problem. It’s not going to go away, and in fact it’s only going to get harder,” he added.

DOI: ACM Transactions on Graphics, 2025. 10.1145/3742892  (About DOIs).

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research-roundup:-7-cool-science-stories-we-almost-missed

Research roundup: 7 cool science stories we almost missed

Archaeology, biomimicry, forensics, medieval medicine, Physics, Science, Taxonomy / /

Other July stories: Solving a 150-year-old fossil mystery and the physics of tacking a sailboat.

150-year-old fossil of Palaeocampa anthrax isn’t a sea worm after all. Credit: Christian McCall

It’s a regrettable reality that there is never enough time to cover all the interesting scientific stories we come across each month. In the past, we’ve featured year-end roundups of cool science stories we (almost) missed. This year, we’re experimenting with a monthly collection. July’s list includes the discovery of the tomb of the first Maya king of Caracol in Belize, the fluid dynamics of tacking a sailboat, how to determine how fast blood was traveling when it stained cotton fabric, and how the structure of elephant ears could lead to more efficient indoor temperature control in future building designs, among other fun stories.

Tomb of first king of Caracol found

University of Houston provost and archeologist Diane Chase in newly discovered tomb of the first ruler of the ancient Maya city Caracol and the founder of its royal dynasty.

Credit: Caracol Archeological Project/University of Houston

Archaeologists Arlen and Diane Chase are the foremost experts on the ancient Maya city of Caracol in Belize and are helping to pioneer the use of airborne LiDAR to locate hidden structures in dense jungle, including a web of interconnected roadways and a cremation site in the center of the city’s Northeast Acropolis plaza. They have been painstakingly excavating the site since the mid-1980s. Their latest discovery is the tomb of Te K’ab Chaak, Caracol’s first ruler, who took the throne in 331 CE and founded a dynasty that lasted more than 460 years.

This is the first royal tomb the husband-and-wife team has found in their 40+ years of excavating the Caracol site. Te K’ab Chaak’s tomb (containing his skeleton) was found at the base of a royal family shrine, along with pottery vessels, carved bone artifacts, jadeite jewelry, and a mosaic jadeite death mask. The Chases estimate that the ruler likely stood about 5’7″ tall and was probably quite old when he died, given his lack of teeth. The Chases are in the process of reconstructing the death mask and conducting DNA and stable isotope analysis of the skeleton.

How blood splatters on clothing

Cast-off blood stain pattern

Credit: Jimmy Brown/CC BY 2.0

Analyzing blood splatter patterns is a key focus in forensic science, and physicists have been offering their expertise for several years now, including in two 2019 studies on splatter patterns from gunshot wounds. The latest insights gleaned from physics concern the distinct ways in which blood stains cotton fabrics, according to a paper published in Forensic Science International.

Blood is a surprisingly complicated fluid, in part because the red blood cells in human blood can form long chains, giving it the consistency of sludge. And blood starts to coagulate immediately once it leaves the body. Blood is also viscoelastic: not only does it deform slowly when exposed to an external force, but once that force has been removed, it will return to its original configuration. Add in coagulation and the type of surface on which it lands, and correctly interpreting the resulting spatter patterns becomes incredibly difficult.

The co-authors of the July study splashed five different fabric surfaces with pig’s blood at varying velocities, capturing the action with high-speed cameras. They found that when a blood stain has “fingers” spreading out from the center, the more fingers there are, the faster the blood was traveling when it struck the fabric. And the faster the blood was moving, the more “satellite droplets” there will be—tiny stains surrounding the central stain. Finally, it’s much easier to estimate the velocity of blood splatter on plain-woven cotton than on other fabrics like twill. The researchers plan to extend future work to include a wider variety of fabrics, weaves, and yarns.

DOI: Forensic Science International, 2025. 10.1016/j.forsciint.2025.112543  (About DOIs).

Offshore asset practices of the uber-rich

The uber-rich aren’t like the rest of us in so many ways, including their canny exploitation of highly secretive offshore financial systems to conceal their assets and/or identities. Researchers at Dartmouth have used machine learning to analyze two public databases and identified distinct patterns in the strategies oligarchs and billionaires in 65 different countries employ when squirreling away offshore assets, according to a paper published in the journal PLoS ONE.

One database tracks offshore finance, while the other rates different countries on their “rule of law.” This enabled the team to study key metrics like how much of their assets elites move offshore, how much they diversify, and how much they make use of “blacklisted” offshore centers that are not part of the mainstream financial system. The researchers found three distinct patterns, all tied to where an oligarch comes from.

Billionaires from authoritarian countries are more likely to diversify their hidden assets across many different centers—a “confetti strategy”—perhaps because these are countries likely to exact political retribution. Others, from countries with effective government regulations—or where there is a pronounced lack of civil rights—are more likely to employ a “concealment strategy” that includes more blacklisted jurisdictions, relying more on bearer shares that protect their anonymity. Those elites most concerned about corruption and/or having their assets seized typically employ a hybrid strategy.

The work builds on an earlier 2023 study concluding that issuing sanctions on individual oligarchs in Russia, China, the US, and Hong Kong is less effective than targeting the small, secretive network of financial experts who manage that wealth on behalf of the oligarchs. That’s because sanctioning just one wealth manager effectively takes out several oligarchs at once, per the authors.

DOI: PLoS ONE, 2025. 10.1371/journal.pone.0326228  (About DOIs).

Medieval remedies similar to TikTok trends

Medieval manuscripts like the Cotton MS Vitellius C III highlight uses for herbs that reflect modern-day wellness trends.

Credit: The British Library

The Middle Ages are stereotypically described as the “Dark Ages,” with a culture driven by superstition—including its medical practices. But a perusal of the hundreds of medical manuscripts collected in the online Corpus of Early Medieval Latin Medicine (CEMLM) reveals that in many respects, medical practices were much more sophisticated; some of the remedies are not much different from alternative medicine remedies touted by TikTok influencers today. That certainly doesn’t make them medically sound, but it does suggest we should perhaps not be too hasty in who we choose to call backward and superstitious.

Per Binghamton University historian Meg Leja, medievalists were not “anti-science.” In fact, they were often quite keen on learning from the natural world. And their health practices, however dubious they might appear to us—lizard shampoo, anyone?—were largely based on the best knowledge available at the time. There are detox cleanses and topical ointments, such as crushing the stone of a peach, mixing it with rose oil, and smearing it on one’s forehead to relieve migraine pain. (Rose oil may actually be an effective migraine pain reliever.) The collection is well worth perusing; pair it with the Wellcome-funded Curious Cures in Cambridge Libraries to learn even more about medieval medical recipes.

Physics of tacking a sailboat

The Courant Institute's Christiana Mavroyiakoumou, above at Central Park's Conservatory Water with model sailboats

Credit: Jonathan King/NYU

Possibly the most challenging basic move for beginner sailors is learning how to tack to sail upwind. Done correctly, the sail will flip around into a mirror image of its previous shape. And in competitive sailboat racing, a bad tack can lose the race. So physicists at the University of Michigan decided to investigate the complex fluid dynamics at play to shed more light on the tricky maneuver, according to a paper published in the journal Physical Review Fluids.

After modeling the maneuver and conducting numerical simulations, the physicists concluded that there are three primary factors that determine a successful tack: the stiffness of the sail, its tension before the wind hits, and the final sail angle in relation to the direction of the wind. Ideally, one wants a less flexible, less curved sail with high tension prior to hitting the wind and to end up with a 20-degree final sail angle. Other findings: It’s harder to flip a slack sail when tacking, and how fast one manages to flip the sail depends on the sail’s mass and the speed and acceleration of the turn.

DOI: Physical Review Fluids, 2025. 10.1103/37xg-vcff  (About DOIs).

Elephant ears inspire building design

African bush elephant with ears spread in a threat or attentive position and visible blood vessels

Maintaining a comfortable indoor temperature constitutes the largest fraction of energy usage for most buildings, with the surfaces of walls, windows, and ceilings contributing to roughly 63 percent of energy loss. Engineers at Drexel University have figured out how to make surfaces that help rather than hamper efforts to maintain indoor temperatures: using so-called phase-change materials that can absorb and release thermal energy as needed as they shift between liquid and solid states. They described the breakthrough in a paper published in the Journal of Building Engineering.

The Drexel group previously developed a self-warming concrete using a paraffin-based material, similar to the stuff used to make candles. The trick this time around, they found, was to create the equivalent of a vascular network within cement-based building materials. They used a printed polymer matrix to create a grid of channels in the surface of concrete and filled those channels with the same paraffin-based material. When temperatures drop, the material turns into a solid and releases heat energy; as temperatures rise, it shifts its phase to a liquid and absorbs heat energy.

The group tested several different configurations and found that the most effective combination of strength and thermal regulation was realized with a diamond-shaped grid, which boasted the most vasculature surface area. This configuration successfully slowed the cooling and heating of its surface to between 1 and 1.2 degrees Celsius per hour, while holding up against stretching and compression tests. The structure is similar to that of jackrabbit and elephant ears, which have extensive vascular networks to help regulate body temperature.

DOI: Journal of Building Engineering, 2025. 10.1016/j.jobe.2025.112878  (About DOIs).

ID-ing a century-old museum specimen

Neotype of Palaeocampa anthrax from the Mazon Creek Lagerstätte and rediscovered in the Invertebrate Paleontology collection of the MCZ.

Credit: Richard J. Knecht

Natural history museums have lots of old specimens in storage, and revisiting those specimens can sometimes lead to new discoveries. That’s what happened to University of Michigan evolutionary biologist Richard J. Knecht as he was poring over a collection at Harvard’s Museum of Comparative Zoology while a grad student there. One of the fossils, originally discovered in 1865, was labeled a millipede. But Knecht immediately recognized it as a type of lobopod, according to a paper published in the journal Communications Biology. It’s the earliest lobopod yet found, and this particular species also marks an evolutionary leap since it’s the first known lobopod to be non-marine.

Lobopods are the evolutionary ancestors to arthropods (insects, spiders, and crustaceans), and their fossils are common along Paleozoic sea beds. Apart from tardigrades and velvet worms, however, they were thought to be confined to oceans. But Palaeocampa anthrax has legs on every trunk, as well as almost 1,000 bristly spines covering its body with orange halos at their tips. Infrared spectroscopy revealed traces of fossilized molecules—likely a chemical that emanated from the spinal tips. Since any chemical defense would just disperse in water, limiting its effectiveness, Knecht concluded that Palaeocampa anthrax was most likely amphibious rather than being solely aquatic.

DOI: Communications Biology, 2025. 10.1038/s42003-025-08483-0  (About DOIs).

Photo of Jennifer Ouellette

Jennifer is a senior writer at Ars Technica with a particular focus on where science meets culture, covering everything from physics and related interdisciplinary topics to her favorite films and TV series. Jennifer lives in Baltimore with her spouse, physicist Sean M. Carroll, and their two cats, Ariel and Caliban.

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peacock-feathers-can-emit-laser-beams

Peacock feathers can emit laser beams

biolasers, biomimicry, lasers, photonic crystals, photonics, Physics, Science, structural color / /

Peacock feathers are greatly admired for their bright iridescent colors, but it turns out they can also emit laser light when dyed multiple times, according to a paper published in the journal Scientific Reports. Per the authors, it’s the first example of a biolaser cavity within the animal kingdom.

As previously reported, the bright iridescent colors in things like peacock feathers and butterfly wings don’t come from any pigment molecules but from how they are structured. The scales of chitin (a polysaccharide common to insects) in butterfly wings, for example, are arranged like roof tiles. Essentially, they form a diffraction grating, except photonic crystals only produce certain colors, or wavelengths, of light, while a diffraction grating will produce the entire spectrum, much like a prism.

In the case of peacock feathers, it’s the regular, periodic nanostructures of the barbules—fiber-like components composed of ordered melanin rods coated in keratin—that produce the iridescent colors. Different colors correspond to different spacing of the barbules.

Both are naturally occurring examples of what physicists call photonic crystals. Also known as photonic bandgap materials, photonic crystals are “tunable,” which means they are precisely ordered in such a way as to block certain wavelengths of light while letting others through. Alter the structure by changing the size of the tiles, and the crystals become sensitive to a different wavelength. (In fact, the rainbow weevil can control both the size of its scales and how much chitin is used to fine-tune those colors as needed.)

Even better (from an applications standpoint), the perception of color doesn’t depend on the viewing angle. And the scales are not just for aesthetics; they help shield the insect from the elements. There are several types of manmade photonic crystals, but gaining a better and more detailed understanding of how these structures grow in nature could help scientists design new materials with similar qualities, such as iridescent windows, self-cleaning surfaces for cars and buildings, or even waterproof textiles. Paper currency could incorporate encrypted iridescent patterns to foil counterfeiters.

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Merger of two massive black holes is one for the record books

astronomy, astrophysics, black hole mergers, black holes, gravitational waves, LIGO/VIRGO/KAGRA, Physics, Science / /

Physicists with the LIGO/Virgo/KAGRA collaboration have detected the gravitational wave signal (dubbed GW231123) of the most massive merger between two black holes yet observed, resulting in a new black hole that is 225 times more massive than our Sun. The results were presented at the Edoardo Amaldi Conference on Gravitational Waves in Glasgow, Scotland.

The LIGO/Virgo/KAGRA collaboration searches the universe for gravitational waves produced by the mergers of black holes and neutron stars. LIGO detects gravitational waves via laser interferometry, using high-powered lasers to measure tiny changes in the distance between two objects positioned kilometers apart. LIGO has detectors in Hanford, Washington, and in Livingston, Louisiana. A third detector in Italy, Advanced Virgo, came online in 2016. In Japan, KAGRA is the first gravitational-wave detector in Asia and the first to be built underground. Construction began on LIGO-India in 2021, and physicists expect it will turn on sometime after 2025.

To date, the collaboration has detected dozens of merger events since its first Nobel Prize-winning discovery. Early detected mergers involved either two black holes or two neutron stars.  In 2021, LIGO/Virgo/KAGRA confirmed the detection of two separate “mixed” mergers between black holes and neutron stars.

A tour of Virgo. Credit: EGO-Virgo

LIGO/Virgo/KAGRA started its fourth observing run in 2023, and by the following year had announced the detection of a signal indicating a merger between two compact objects, one of which was most likely a neutron star. The other had an intermediate mass—heavier than a neutron star and lighter than a black hole. It was the first gravitational-wave detection of a mass-gap object paired with a neutron star and hinted that the mass gap might be less empty than astronomers previously thought.

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Microsoft lays out its path to useful quantum computing

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Its platform needs error correction that works with different hardware.

Some of the optical hardware needed to make Atom Computing’s machines work. Credit: Atom Computing

On Thursday, Microsoft’s Azure Quantum group announced that it has settled on a plan for getting error correction on quantum computers. While the company pursues its own hardware efforts, the Azure team is a platform provider that currently gives access to several distinct types of hardware qubits. So it has chosen a scheme that is suitable for several different quantum computing technologies (notably excluding its own). The company estimates that the system it has settled on can take hardware qubits with an error rate of about 1 in 1,000 and use them to build logical qubits where errors are instead 1 in 1 million.

While it’s describing the scheme in terms of mathematical proofs and simulations, it hasn’t shown that it works using actual hardware yet. But one of its partners, Atom Computing, is accompanying the announcement with a description of how its machine is capable of performing all the operations that will be needed.

Arbitrary connections

There are similarities and differences between what the company is talking about today and IBM’s recent update of its roadmap, which described another path to error-resistant quantum computing. In IBM’s case, it makes both the software stack that will perform the error correction and the hardware needed to implement it. It uses chip-based hardware, with the connections among qubits mediated by wiring that’s laid out when the chip is fabricated. Since error correction schemes require a very specific layout of connections among qubits, once IBM decides on a quantum error correction scheme, it can design chips with the wiring needed to implement that scheme.

Microsoft’s Azure, in contrast, provides its users with access to hardware from several different quantum computing companies, each based on different technology. Some of them, like Rigetti and Microsoft’s own planned processor, are similar to IBM’s in that they have a fixed layout during manufacturing, and so can only handle codes that are compatible with their wiring layout. But others, such as those provided by Quantinuum and Atom Computing, store their qubits in atoms that can be moved around and connected in arbitrary ways. Those arbitrary connections allow very different types of error correction schemes to be considered.

It can be helpful to think of this using an analogy to geometry. A chip is like a plane, where it’s easiest to form the connections needed for error correction among neighboring qubits; longer connections are possible, but not as easy. Things like trapped ions and atoms provide a higher-dimensional system where far more complicated patterns of connections are possible. (Again, this is an analogy. IBM is using three-dimensional wiring in its processing chips, while Atom Computing stores all its atoms in a single plane.)

Microsoft’s announcement is focused on the sorts of processors that can form the more complicated, arbitrary connections. And, well, it’s taking full advantage of that, building an error correction system with connections that form a four-dimensional hypercube. “We really have focused on the four-dimensional codes due to their amenability to current and near term hardware designs,” Microsoft’s Krysta Svore told Ars.

The code not only describes the layout of the qubits and their connections, but also the purpose of each hardware qubit. Some of them are used to hang on to the value of the logical qubit(s) stored in a single block of code. Others are used for what are called “weak measurements.” These measurements tell us something about the state of the ones that are holding on to the data—not enough to know their values (a measurement that would end the entanglement), but enough to tell if something has changed. The details of the measurement allow corrections to be made that restore the original value.

Microsoft’s error correction system is described in a preprint that the company recently released. It includes a family of related geometries, each of which provides different degrees of error correction, based on how many simultaneous errors they can identify and fix. The descriptions are about what you’d expect for complicated math and geometry—”Given a lattice Λ with an HNF L, the code subspace of the 4D geometric code CΛ is spanned by the second homology H2(T4Λ,F2) of the 4-torus T4Λ—but the gist is that all of them convert collections of physical qubits into six logical qubits that can be error corrected.

The more hardware qubits you add to host those six logical qubits, the greater error protection each of them gets. That becomes important because some more sophisticated algorithms will need more than the one-in-a-million error protection that Svore said Microsoft’s favored version will provide. That favorite is what’s called the Hadamard version, which bundles 96 hardware qubits to form six logical qubits, and has a distance of eight (distance being a measure of how many simultaneous errors it can tolerate). You can compare that with IBM’s announcement, which used 144 hardware qubits to host 12 logical qubits at a distance of 12 (so, more hardware, but more logical qubits and greater error resistance).

The other good stuff

On its own, a description of the geometry is not especially exciting. But Microsoft argues that this family of error correction codes has a couple of significant advantages. “All of these codes in this family are what we call single shot,” Svore said. “And that means that, with a very low constant number of rounds of getting information about the noise, one can decode and correct the errors. This is not true of all codes.”

Limiting the number of measurements needed to detect errors is important. For starters, measurements themselves can create errors, so making fewer makes the system more robust. In addition, in things like neutral atom computers, the atoms have to be moved to specific locations where measurements take place, and the measurements heat them up so that they can’t be reused until cooled. So, limiting the measurements needed can be very important for the performance of the hardware.

The second advantage of this scheme, as described in the draft paper, is the fact that you can perform all the operations needed for quantum computing on the logical qubits these schemes host. Just like in regular computers, all the complicated calculations performed on a quantum computer are built up from a small number of simple logical operations. But not every possible logical operation works well with any given error correction scheme. So it can be non-trivial to show that an error correction scheme is compatible with enough of the small operations to enable universal quantum computation.

So, the paper describes how some logical operations can be performed relatively easily, while a few others require manipulations of the error correction scheme in order to work. (These manipulations have names like lattice surgery and magic state distillation, which are good signs that the field doesn’t take itself that seriously.)

So, in sum, Microsoft feels that it has identified an error correction scheme that is fairly compact, can be implemented efficiently on hardware that stores qubits in photons, atoms, or trapped ions, and enables universal computation. What it hasn’t done, however, is show that it actually works. And that’s because it simply doesn’t have the hardware right now. Azure is offering trapped ion machines from IonQ and Qantinuum, but these top out at 56 qubits—well below the 96 needed for their favored version of these 4D codes. The largest it has access to is a 100-qubit machine from a company called PASQAL, which barely fits the 96 qubits needed, leaving no room for error.

While it should be possible to test smaller versions of codes in the same family, the Azure team has already demonstrated its ability to work with error correction codes based on hypercubes, so it’s unclear whether there’s anything to gain from that approach.

More atoms

Instead, it appears to be waiting for another partner, Atom Computing, to field its next-generation machine, one it’s designing in partnership with Microsoft. “This first generation that we are building together between Atom Computing and Microsoft will include state-of-the-art quantum capabilities, will have 1,200 physical qubits,” Svore said “And then the next upgrade of that machine will have upwards of 10,000. And so you’re looking at then being able to go to upwards of a hundred logical qubits with deeper and more reliable computation available. “

So, today’s announcement was accompanied by an update on progress from Atom Computing, focusing on a process called “midcircuit measurement.” Normally, during quantum computing algorithms, you have to resist performing any measurements of the value of qubits until the entire calculation is complete. That’s because quantum calculations depend on things like entanglement and each qubit being in a superposition between its two values; measurements can cause all that to collapse, producing definitive values and ending entanglement.

Quantum error correction schemes, however, require that some of the hardware qubits undergo weak measurements multiple times while the computation is in progress. Those are quantum measurements taking place in the middle of a computation—midcircuit measurements, in other words. To show that its hardware will be up to the task that Microsoft expects of it, the company decided to demonstrate mid-circuit measurements on qubits implementing a simple error correction code.

The process reveals a couple of notable features that are distinct from doing this with neutral atoms. To begin with, the atoms being used for error correction have to be moved to a location—the measurement zone—where they can be measured without disturbing anything else. Then, the measurement typically heats up the atom slightly, meaning they have to be cooled back down afterward. Neither of these processes is perfect, and so sometimes an atom gets lost and needs to be replaced with one from a reservoir of spares. Finally, the atom’s value needs to be reset, and it has to be sent back to its place in the logical qubit.

Testing revealed that about 1 percent of the atoms get lost each cycle, but the system successfully replaces them. In fact, they set up a system where the entire collection of atoms is imaged during the measurement cycle, and any atom that goes missing is identified by an automated system and replaced.

Overall, without all these systems in place, the fidelity of a qubit is about 98 percent in this hardware. With error correction turned on, even this simple logical qubit saw its fidelity rise over 99.5 percent. All of which suggests their next computer should be up to some significant tests of Microsoft’s error correction scheme.

Waiting for the lasers

The key questions are when it will be released, and when its successor, which should be capable of performing some real calculations, will follow it? That’s something that’s a challenging question to ask because, more so than some other quantum computing technologies, neutral atom computing is dependent on something that’s not made by the people who build the computers: lasers. Everything about this system—holding atoms in place, moving them around, measuring, performing manipulations—is done with a laser. The lower the noise of the laser (in terms of things like frequency drift and energy fluctuations), the better performance it’ll have.

So, while Atom can explain its needs to its suppliers and work with them to get things done, it has less control over its fate than some other companies in this space.

Photo of John Timmer

John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.

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