Computer science

Microsoft lays out its path to useful quantum computing

atom computing, Computer science, microsoft, neutral atoms, Physics, quantum computing, quantum error correction, quantum mechanics, Science / Kelly Newman / June 21, 2025

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 C_Λ is spanned by the second homology H₂(T⁴_Λ,F₂) of the 4-torus T⁴_Λ—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.

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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IBM now describing its first error-resistant quantum compute system

Computer science, computing, ibm, logical qubits, Physics, quantum computing, quantum mechanics, Science / Beth Washington / June 10, 2025

Company is moving past focus on qubits, shifting to functional compute units.

A rendering of what IBM expects will be needed to house a Starling quantum computer. Credit: IBM

On Tuesday, IBM released its plans for building a system that should push quantum computing into entirely new territory: a system that can both perform useful calculations while catching and fixing errors and be utterly impossible to model using classical computing methods. The hardware, which will be called Starling, is expected to be able to perform 100 million operations without error on a collection of 200 logical qubits. And the company expects to have it available for use in 2029.

Perhaps just as significant, IBM is also committing to a detailed description of the intermediate steps to Starling. These include a number of processors that will be configured to host a collection of error-corrected qubits, essentially forming a functional compute unit. This marks a major transition for the company, as it involves moving away from talking about collections of individual hardware qubits and focusing instead on units of functional computational hardware. If all goes well, it should be possible to build Starling by chaining a sufficient number of these compute units together.

“We’re updating [our roadmap] now with a series of deliverables that are very precise,” IBM VP Jay Gambetta told Ars, “because we feel that we’ve now answered basically all the science questions associated with error correction and it’s becoming more of a path towards an engineering problem.”

New architectures

Error correction on quantum hardware involves entangling a group of qubits in a way that distributes one or more quantum bit values among them and includes additional qubits that can be used to check the state of the system. It can be helpful to think of these as data and measurement qubits. Performing weak quantum measurements on the measurement qubits produces what’s called “syndrome data,” which can be interpreted to determine whether anything about the data qubits has changed (indicating an error) and how to correct it.

There are lots of potential ways to arrange different combinations of data and measurement qubits for this to work, each referred to as a code. But, as a general rule, the more hardware qubits committed to the code, the more robust it will be to errors, and the more logical qubits that can be distributed among its hardware qubits.

Some quantum hardware, like that based on trapped ions or neutral atoms, is relatively flexible when it comes to hosting error-correction codes. The hardware qubits can be moved around so that any two can be entangled, so it’s possible to adopt a huge range of configurations, albeit at the cost of the time spent moving atoms around. IBM’s technology is quite different. It relies on qubits made of superconducting electronics laid out on a chip, with entanglement mediated by wiring that runs between qubits. The layout of this wiring is set during the chip’s manufacture, and so the chip’s design commits it to a limited number of potential error-correction codes.

Unfortunately, this wiring can also enable crosstalk between neighboring qubits, causing them to lose their state. To avoid this, existing IBM processors have their qubits wired in what they term a “heavy hex” configuration, named for its hexagonal arrangements of connections among its qubits. This has worked well to keep the error rate of its hardware down, but it also poses a challenge, since IBM has decided to go with an error-correction code that’s incompatible with the heavy hex geometry.

A couple of years back, an IBM team described a compact error correction code called a low-density parity check (LDPC). This requires a square grid of nearest-neighbor connections among its qubits, as well as wiring to connect qubits that are relatively distant on the chip. To get its chips and error-correction scheme in sync, IBM has made two key advances. The first is in its chip packaging, which now uses several layers of wiring sitting above the hardware qubits to enable all of the connections needed for the LDPC code.

We’ll see that first in a processor called Loon that’s on the company’s developmental roadmap. “We’ve already demonstrated these three things: high connectivity, long-range couplers, and couplers that break the plane [of the chip] and connect to other qubits,” Gambetta said. “We have to combine them all as a single demonstration showing that all these parts of packaging can be done, and that’s what I want to achieve with Loon.” Loon will be made public later this year.

Two diagrams of blue objects linked by red lines. The one on the left is sparse and simple, while the one on the right is a complicated mesh of red lines. — On the left, the simple layout of the connections in a current-generation Heron processor. At right, the complicated web of connections that will be present in Loon. Credit: IBM

The second advance IBM has made is to eliminate the crosstalk that the heavy hex geometry was used to minimize, so heavy hex will be going away. “We are releasing this year a bird for near-term experiments that is a square array that has almost zero crosstalk,” Gambetta said, “and that is Nighthawk.” The more densely connected qubits cut the overhead needed to perform calculations by a factor of 15, Gambetta told Ars.

Nighthawk is a 2025 release on a parallel roadmap that you can think of as user-facing. Iterations on its basic design will be released annually through 2028, each enabling more operations without error (going from 5,000 gate operations this year to 15,000 in 2028). Each individual Nighthawk processor will host 120 hardware qubits, but 2026 will see three of them chained together and operating as a unit, providing 360 hardware qubits. That will be followed in 2027 by a machine with nine linked Nighthawk processors, boosting the hardware qubit number over 1,000.

Riding the bicycle

The real future of IBM’s hardware, however, will be happening over on the developmental line of processors, where talk about hardware qubit counts will become increasingly irrelevant. In a technical document released today, IBM is describing the specific LDPC code it will be using, termed a bivariate bicycle code due to some cylindrical symmetries in its details that vaguely resemble bicycle wheels. The details of the connections matter less than the overall picture of what it takes to use this error code in practice.

IBM describes two implementations of this form of LDPC code. In the first, 144 hardware qubits are arranged so that they play host to 12 logical qubits and all of the measurement qubits needed to perform error checks. The standard measure of a code’s ability to catch and correct errors is called its distance, and in this case, the distance is 12. As an alternative, they also describe a code that uses 288 hardware qubits to host the same 12 logical qubits but boost the distance to 18, meaning it’s more resistant to errors. IBM will make one of these collections of logical qubits available as a Kookaburra processor in 2026, which will use them to enable stable quantum memory.

The follow-on will bundle these with a handful of additional qubits that can produce quantum states that are needed for some operations. Those, plus hardware needed for the quantum memory, form a single, functional computation unit, built on a single chip, that is capable of performing all the operations needed to implement any quantum algorithm.

That will appear with the Cockatoo chip, which will also enable multiple processing units to be linked on a single bus, allowing the logical qubit count to grow beyond 12. (The company says that one of the dozen logical qubits in each unit will be used to mediate entanglement with other units and so won’t be available for computation.) That will be followed by the first test versions of Starling, which will allow universal computations on a limited number of logical qubits spread across multiple chips.

Separately, IBM is releasing a document that describes a key component of the system that will run on classical computing hardware. Full error correction requires evaluating the syndrome data derived from the state of all the measurement qubits in order to determine the state of the logical qubits and whether any corrections need to be made. As the complexity of the logical qubits grows, the computational burden of evaluating grows with it. If this evaluation can’t be executed in real time, then it becomes impossible to perform error-corrected calculations.

To address this, IBM has developed a message-passing decoder that can perform parallel evaluations of the syndrome data. The system explores more of the solution space by a combination of randomizing the weight given to the memory of past solutions and by handing any seemingly non-optimal solutions on to new instances for additional evaluation. The key thing is that IBM estimates that this can be run in real time using FPGAs, ensuring that the system works.

A quantum architecture

There are a lot more details beyond those, as well. Gambetta described the linkage between each computational unit—IBM is calling it a Universal Bridge—which requires one microwave cable for each code distance of the logical qubits being linked. (In other words, a distance 12 code would need 12 microwave-carrying cables to connect each chip.) He also said that IBM is developing control hardware that can operate inside the refrigeration hardware, based on what they’re calling “cold CMOS,” which is capable of functioning at 4 Kelvin.

The company is also releasing renderings of what it expects Starling to look like: a series of dilution refrigerators, all connected by a single pipe that contains the Universal Bridge. “It’s an architecture now,” Gambetta said. “I have never put details in the roadmap that I didn’t feel we could hit, and now we’re putting a lot more details.”

The striking thing to me about this is that it marks a shift away from a focus on individual qubits, their connectivity, and their error rates. The error hardware rates are now good enough (4 x 10^-4) for this to work, although Gambetta felt that a few more improvements should be expected. And connectivity will now be directed exclusively toward creating a functional computational unit.

That said, there’s still a lot of space beyond Starling on IBM’s roadmap. The 200 logical qubits it promises will be enough to handle some problems, but not enough to perform the complex algorithms needed to do things like break encryption. That will need to wait for something closer to Blue Jay, a 2033 system that IBM expects will have 2,000 logical qubits. And, as of right now, it’s the only thing listed beyond Starling.

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Startup puts a logical qubit in a single piece of hardware

Computer science, nord quantique, Physics, quantum computing, quantum mechanics, Science / Beth Washington / June 6, 2025

A bit over a year ago, Nord Quantique used a similar setup to show that it could be used to identify the most common form of error in these devices, one in which the system loses one of its photons. “We can store multiple microwave photons into each of these cavities, and the fact that we have redundancy in the system comes exactly from this,” said Nord Quantique’s CTO, Julien Camirand Lemyre. However, this system was unable to handle many of the less common errors that might also occur.

This time around, the company is showing that it can get an actual logical qubit into a variant of the same hardware. In the earlier version of its equipment, the resonator cavity had a single post and supported a single frequency. In the newer iteration, there were two posts and two frequencies. Each of those frequencies creates its own quantum resonator in the same cavity, with its own set of modes. “It’s this ensemble of photons inside this cavity that creates the logical qubit,” Lemyre told Ars.

The additional quantum information that can now be stored in the system enables it to identify more complex errors than the loss of a photon.

Catching, but not fixing errors

The company did two experiments with this new hardware. First, it ran multiple rounds of error detection on data stored in the logical qubit, essentially testing its ability to act like a quantum memory and retain the information stored there. Without correcting errors, the system rapidly decayed, with an error probability in each round of measurement of about 12 percent. By the time the system reached the 25th measurement, almost every instance had already encountered an error.

The second time through, the company repeated the process, discarding any instances in which an error occurred. In almost every instance, that meant the results were discarded long before they got through two dozen rounds of measurement. But at these later stages, none of the remaining instances were in an erroneous state. That indicates that a successful correction of the errors—something the team didn’t try—would be able to fix all the detected problems.

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Quantum hardware may be a good match for AI

AI, Computer science, image classification, Physics, quantum computing, quantum mechanics, Science / Kelly Newman / April 11, 2025

Quantum computers don’t have that sort of separation. While they could include some quantum memory, the data is generally housed directly in the qubits, while computation involves performing operations, called gates, directly on the qubits themselves. In fact, there has been a demonstration that, for supervised machine learning, where a system can learn to classify items after training on pre-classified data, a quantum system can outperform classical ones, even when the data being processed is housed on classical hardware.

This form of machine learning relies on what are called variational quantum circuits. This is a two-qubit gate operation that takes an additional factor that can be held on the classical side of the hardware and imparted to the qubits via the control signals that trigger the gate operation. You can think of this as analogous to the communications involved in a neural network, with the two-qubit gate operation equivalent to the passing of information between two artificial neurons and the factor analogous to the weight given to the signal.

That’s exactly the system that a team from the Honda Research Institute worked on in collaboration with a quantum software company called Blue Qubit.

Pixels to qubits

The focus of the new work was mostly on how to get data from the classical world into the quantum system for characterization. But the researchers ended up testing the results on two different quantum processors.

The problem they were testing is one of image classification. The raw material was from the Honda Scenes dataset, which has images taken from roughly 80 hours of driving in Northern California; the images are tagged with information about what’s in the scene. And the question the researchers wanted the machine learning to handle was a simple one: Is it snowing in the scene?

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Microsoft demonstrates working qubits based on exotic physics

Apple, Computer science, Majorana, Physics, quantum computing, quantum mechanics, quasiparticles, Science / Mike M. / February 20, 2025

Microsoft’s first entry into quantum hardware comes in the form of Majorana 1, a processor with eight of these qubits.

Given that some of its competitors have hardware that supports over 1,000 qubits, why does the company feel it can still be competitive? Nayak described three key features of the hardware that he feels will eventually give Microsoft an advantage.

The first has to do with the fundamental physics that governs the energy needed to break apart one of the Cooper pairs in the topological superconductor, which could destroy the information held in the qubit. There are a number of ways to potentially increase this energy, from lowering the temperature to making the indium arsenide wire longer. As things currently stand, Nayak said that small changes in any of these can lead to a large boost in the energy gap, making it relatively easy to boost the system’s stability.

Another key feature, he argued, is that the hardware is relatively small. He estimated that it should be possible to place a million qubits on a single chip. “Even if you put in margin for control structures and wiring and fan out, it’s still a few centimeters by a few centimeters,” Nayak said. “That was one of the guiding principles of our qubits.” So unlike some other technologies, the topological qubits won’t require anyone to figure out how to link separate processors into a single quantum system.

Finally, all the measurements that control the system run through the quantum dot, and controlling that is relatively simple. “Our qubits are voltage-controlled,” Nayak told Ars. “What we’re doing is just turning on and off coupling of quantum dots to qubits to topological nano wires. That’s a digital signal that we’re sending, and we can generate those digital signals with a cryogenic controller. So we actually put classical control down in the cold.”

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AI used to design a multi-step enzyme that can digest some plastics

AI, biochemistry, Biology, catalysts, chemistry, Computer science, enzymes, Science / Mike M. / February 16, 2025

And it worked. Repeating the same process with an added PLACER screening step boosted the number of enzymes with catalytic activity by over three-fold.

Unfortunately, all of these enzymes stalled after a single reaction. It turns out they were much better at cleaving the ester, but they left one part of it chemically bonded to the enzyme. In other words, the enzymes acted like part of the reaction, not a catalyst. So the researchers started using PLACER to screen for structures that could adopt a key intermediate state of the reaction. This produced a much higher rate of reactive enzymes (18 percent of them cleaved the ester bond), and two—named “super” and “win”—could actually cycle through multiple rounds of reactions. The team had finally made an enzyme.

By adding additional rounds alternating between structure suggestions using RFDiffusion and screening using PLACER, the team saw the frequency of functional enzymes increase and eventually designed one that had an activity similar to some produced by actual living things. They also showed they could use the same process to design an esterase capable of digesting the bonds in PET, a common plastic.

If that sounds like a lot of work, it clearly was—designing enzymes, especially ones where we know of similar enzymes in living things, will remain a serious challenge. But at least much of it can be done on computers rather than requiring someone to order up the DNA that encodes the enzyme, getting bacteria to make it, and screening for activity. And despite the process involving references to known enzymes, the designed ones didn’t share a lot of sequences in common with them. That suggests there should be added flexibility if we want to design one that will react with esters that living things have never come across.

I’m curious about what might happen if we design an enzyme that is essential for survival, put it in bacteria, and then allow it to evolve for a while. I suspect life could find ways of improving on even our best designs.

Science, 2024. DOI: 10.1126/science.adu2454 (About DOIs).

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Quantum teleportation used to distribute a calculation

Computer science, Physics, quantum computing, quantum mechanics, Science, teleportation / 9u50fv / February 6, 2025

The researchers showed that this setup allowed them to teleport with a specific gate operation (controlled-Z), which can serve as the basis for any other two-qubit gate operation—any operation you might want to do can be done by using a specific combination of these gates. After performing multiple rounds of these gates, the team found that the typical fidelity was in the area of 70 percent. But they also found that errors typically had nothing to do with the teleportation process and were the product of local operations at one of the two ends of the network. They suspect that using commercial hardware, which has far lower error rates, would improve things dramatically.

Finally, they performed a version of Grover’s algorithm, which can, with a single query, identify a single item from an arbitrarily large unordered list. The “arbitrary” aspect is set by the number of available qubits; in this case, having only two qubits, the list maxed out at four items. Still, it worked, again with a fidelity of about 70 percent.

While the work was done with trapped ions, almost every type of qubit in development can be controlled with photons, so the general approach is hardware-agnostic. And, given the sophistication of our optical hardware, it should be possible to link multiple chips at various distances, all using hardware that doesn’t require the best vacuum or the lowest temperatures we can generate.

That said, the error rate of the teleportation steps may still be a problem, even if it was lower than the basic hardware rate in these experiments. The fidelity there was 97 percent, which is lower than the hardware error rates of most qubits and high enough that we couldn’t execute too many of these before the probability of errors gets unacceptably high.

Still, our current hardware error rates started out far worse than they are today; successive rounds of improvements between generations of hardware have been the rule. Given that this is the first demonstration of teleported gates, we may have to wait before we can see if the error rates there follow a similar path downward.

Nature, 2025. DOI: 10.1038/s41586-024-08404-x (About DOIs).

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To help AIs understand the world, researchers put them in a robot

AI, Computer science, language, robotics, Science / Shannon Garcia / February 2, 2025

There’s a difference between knowing a word and knowing a concept.

Large language models like ChatGPT display conversational skills, but the problem is they don’t really understand the words they use. They are primarily systems that interact with data obtained from the real world but not the real world itself. Humans, on the other hand, associate language with experiences. We know what the word “hot” means because we’ve been burned at some point in our lives.

Is it possible to get an AI to achieve a human-like understanding of language? A team of researchers at the Okinawa Institute of Science and Technology built a brain-inspired AI model comprising multiple neural networks. The AI was very limited—it could learn a total of just five nouns and eight verbs. But their AI seems to have learned more than just those words; it learned the concepts behind them.

Babysitting robotic arms

“The inspiration for our model came from developmental psychology. We tried to emulate how infants learn and develop language,” says Prasanna Vijayaraghavan, a researcher at the Okinawa Institute of Science and Technology and the lead author of the study.

While the idea of teaching AIs the same way we teach little babies is not new—we applied it to standard neural nets that associated words with visuals. Researchers also tried teaching an AI using a video feed from a GoPro strapped to a human baby. The problem is babies do way more than just associate items with words when they learn. They touch everything—grasp things, manipulate them, throw stuff around, and this way, they learn to think and plan their actions in language. An abstract AI model couldn’t do any of that, so Vijayaraghavan’s team gave one an embodied experience—their AI was trained in an actual robot that could interact with the world.

Vijayaraghavan’s robot was a fairly simple system with an arm and a gripper that could pick objects up and move them around. Vision was provided by a simple RGB camera feeding videos in a somewhat crude 64×64 pixels resolution.

The robot and the camera were placed in a workspace, put in front of a white table with blocks painted green, yellow, red, purple, and blue. The robot’s task was to manipulate those blocks in response to simple prompts like “move red left,” “move blue right,” or “put red on blue.” All that didn’t seem particularly challenging. What was challenging, though, was building an AI that could process all those words and movements in a manner similar to humans. “I don’t want to say we tried to make the system biologically plausible,” Vijayaraghavan told Ars. “Let’s say we tried to draw inspiration from the human brain.”

Chasing free energy

The starting point for Vijayaraghavan’s team was the free energy principle, a hypothesis that the brain constantly makes predictions about the world based on internal models, then updates these predictions based on sensory input. The idea is that we first think of an action plan to achieve a desired goal, and then this plan is updated in real time based on what we experience during execution. This goal-directed planning scheme, if the hypothesis is correct, governs everything we do, from picking up a cup of coffee to landing a dream job.

All that is closely intertwined with language. Neuroscientists at the University of Parma found that motor areas in the brain got activated when the participants in their study listened to action-related sentences. To emulate that in a robot, Vijayaraghavan used four neural networks working in a closely interconnected system. The first was responsible for processing visual data coming from the camera. It was tightly integrated with a second neural net that handled proprioception: all the processes that ensured the robot was aware of its position and the movement of its body. This second neural net also built internal models of actions necessary to manipulate blocks on the table. Those two neural nets were additionally hooked up to visual memory and attention modules that enabled them to reliably focus on the chosen object and separate it from the image’s background.

The third neural net was relatively simple and processed language using vectorized representations of those “move red right” sentences. Finally, the fourth neural net worked as an associative layer and predicted the output of the previous three at every time step. “When we do an action, we don’t always have to verbalize it, but we have this verbalization in our minds at some point,” Vijayaraghavan says. The AI he and his team built was meant to do just that: seamlessly connect language, proprioception, action planning, and vision.

When the robotic brain was up and running, they started teaching it some of the possible combinations of commands and sequences of movements. But they didn’t teach it all of them.

The birth of compositionality

In 2016, Brenden Lake, a professor of psychology and data science, published a paper in which his team named a set of competencies machines need to master to truly learn and think like humans. One of them was compositionality: the ability to compose or decompose a whole into parts that can be reused. This reuse lets them generalize acquired knowledge to new tasks and situations. “The compositionality phase is when children learn to combine words to explain things. They [initially] learn the names of objects, the names of actions, but those are just single words. When they learn this compositionality concept, their ability to communicate kind of explodes,” Vijayaraghavan explains.

The AI his team built was made for this exact purpose: to see if it would develop compositionality. And it did.

Once the robot learned how certain commands and actions were connected, it also learned to generalize that knowledge to execute commands it never heard before. recognizing the names of actions it had not performed and then performing them on combinations of blocks it had never seen. Vijayaraghavan’s AI figured out the concept of moving something to the right or the left or putting an item on top of something. It could also combine words to name previously unseen actions, like putting a blue block on a red one.

While teaching robots to extract concepts from language has been done before, those efforts were focused on making them understand how words were used to describe visuals. Vijayaragha built on that to include proprioception and action planning, basically adding a layer that integrated sense and movement to the way his robot made sense of the world.

But some issues are yet to overcome. The AI had very limited workspace. The were only a few objects and all had a single, cubical shape. The vocabulary included only names of colors and actions, so no modifiers, adjectives, or adverbs. Finally, the robot had to learn around 80 percent of all possible combinations of nouns and verbs before it could generalize well to the remaining 20 percent. Its performance was worse when those ratios dropped to 60/40 and 40/60.

But it’s possible that just a bit more computing power could fix this. “What we had for this study was a single RTX 3090 GPU, so with the latest generation GPU, we could solve a lot of those issues,” Vijayaraghavan argued. That’s because the team hopes that adding more words and more actions won’t result in a dramatic need for computing power. “We want to scale the system up. We have a humanoid robot with cameras in its head and two hands that can do way more than a single robotic arm. So that’s the next step: using it in the real world with real world robots,” Vijayaraghavan said.

Science Robotics, 2025. DOI: 10.1126/scirobotics.adp0751

Jacek Krywko is a freelance science and technology writer who covers space exploration, artificial intelligence research, computer science, and all sorts of engineering wizardry.

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Researchers optimize simulations of molecules on quantum computers

catalysts, Computer science, electrons, Physics, quantum computing, quantum mechanics, Science / Kelly Newman / January 24, 2025

The net result is a much faster operation involving far fewer gates. That’s important because errors in quantum hardware increase as a function of both time and the number of operations.

The researchers then used this approach to explore a chemical, Mn₄O₅Ca, that plays a key role in photosynthesis. Using this approach, they showed it’s possible to calculate what’s called the “spin ladder,” or the list of the lowest-energy states the electrons can occupy. The energy differences between these states correspond to the wavelengths of light they can absorb or emit, so this also defines the spectrum of the molecule.

Faster, but not quite fast enough

We’re not quite ready to run this system on today’s quantum computers, as the error rates are still a bit too high. But because the operations needed to run this sort of algorithm can be done so efficiently, the error rates don’t have to come down very much before the system will become viable. The primary determinant of whether it will run into an error is how far down the time dimension you run the simulation, plus the number of measurements of the system you take over that time.

“The algorithm is especially promising for near-term devices having favorable resource requirements quantified by the number of snapshots (sample complexity) and maximum evolution time (coherence) required for accurate spectral computation,” the researchers wrote.

But the work also makes a couple of larger points. The first is that quantum computers are fundamentally unlike other forms of computation we’ve developed. They’re capable of running things that look like traditional algorithms, where operations are performed and a result is determined. But they’re also quantum systems that are growing in complexity with each new generation of hardware, which makes them great at simulating other quantum systems. And there are a number of hard problems involving quantum systems we’d like to solve.

In some ways, we may only be starting to scratch the surface of quantum computers’ potential. Up until quite recently, there were a lot of hypotheticals; it now appears we’re on the cusp of using one for some potentially useful computations. And that means more people will start thinking about clever ways we can solve problems with them—including cases like this, where the hardware would be used in ways its designers might not have even considered.

Nature Physics, 2025. DOI: 10.1038/s41567-024-02738-z (About DOIs).

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Researchers use AI to design proteins that block snake venom toxins

AI, anti-toxin, biochemistry, Computer science, protein design, Science, snakes, venom / Mike M. / January 15, 2025

Since these two toxicities work through entirely different mechanisms, the researchers tackled them separately.

Blocking a neurotoxin

The neurotoxic three-fingered proteins are a subgroup of the larger protein family that specializes in binding to and blocking the receptors for acetylcholine, a major neurotransmitter. Their three-dimensional structure, which is key to their ability to bind these receptors, is based on three strings of amino acids within the protein that nestle against each other (for those that have taken a sufficiently advanced biology class, these are anti-parallel beta sheets). So to interfere with these toxins, the researchers targeted these strings.

They relied on an AI package called RFdiffusion (the RF denotes its relation to the Rosetta Fold protein-folding software). RFdiffusion can be directed to design protein structures that are complements to specific chemicals; in this case, it identified new strands that could line up along the edge of the ones in the three-fingered toxins. Once those were identified, a separate AI package, called ProteinMPNN, was used to identify the amino acid sequence of a full-length protein that would form the newly identified strands.

But we’re not done with the AI tools yet. The combination of three-fingered toxins and a set of the newly designed proteins were then fed into DeepMind’s AlfaFold2 and the Rosetta protein structure software, and the strength of the interactions between them were estimated.

It’s only at this point that the researchers started making actual proteins, focusing on the candidates that the software suggested would interact the best with the three-fingered toxins. Forty-four of the computer-designed proteins were tested for their ability to interact with the three-fingered toxin, and the single protein that had the strongest interaction was used for further studies.

At this point, it was back to the AI, where RFDiffusion was used to suggest variants of this protein that might bind more effectively. About 15 percent of its suggestions did, in fact, interact more strongly with the toxin. The researchers then made both the toxin and the strongest inhibitor in bacteria and obtained the structure of their interactions. This confirmed that the software’s predictions were highly accurate.

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Meta takes us a step closer to Star Trek’s universal translator

AI, Computer science, human speech, Science, text processing, translation / 9u50fv / January 15, 2025

The computer science behind translating speech from 100 source languages.

In 2023, AI researchers at Meta interviewed 34 native Spanish and Mandarin speakers who lived in the US but didn’t speak English. The goal was to find out what people who constantly rely on translation in their day-to-day activities expect from an AI translation tool. What those participants wanted was basically a Star Trek universal translator or the Babel Fish from the Hitchhiker’s Guide to the Galaxy: an AI that could not only translate speech to speech in real time across multiple languages, but also preserve their voice, tone, mannerisms, and emotions. So, Meta assembled a team of over 50 people and got busy building it.

What this team came up with was a next-gen translation system called Seamless. The first building block of this system is described in Wednesday’s issue of Nature; it can translate speech among 36 different languages.

Language data problems

AI translation systems today are mostly focused on text, because huge amounts of text are available in a wide range of languages thanks to digitization and the Internet. Institutions like the United Nations or European Parliament routinely translate all their proceedings into the languages of all their member states, which means there are enormous databases comprising aligned documents prepared by professional human translators. You just needed to feed those huge, aligned text corpora into neural nets (or hidden Markov models before neural nets became all the rage) and you ended up with a reasonably good machine translation system. But there were two problems with that.

The first issue was those databases comprised formal documents, which made the AI translators default to the same boring legalese in the target language even if you tried to translate comedy. The second problem was speech—none of this included audio data.

The problem of language formality was mostly solved by including less formal sources like books, Wikipedia, and similar material in AI training databases. The scarcity of aligned audio data, however, remained. Both issues were at least theoretically manageable in high-resource languages like English or Spanish, but they got dramatically worse in low-resource languages like Icelandic or Zulu.

As a result, the AI translators we have today support an impressive number of languages in text, but things are complicated when it comes to translating speech. There are cascading systems that simply do this trick in stages. An utterance is first converted to text just as it would be in any dictation service. Then comes text-to-text translation, and finally the resulting text in the target language is synthesized into speech. Because errors accumulate at each of those stages, the performance you get this way is usually poor, and it doesn’t work in real time.

A few systems that can translate speech-to-speech directly do exist, but in most cases they only translate into English and not in the opposite way. Your foreign language interlocutor can say something to you in one of the languages supported by tools like Google’s AudioPaLM, and they will translate that to English speech, but you can’t have a conversation going both ways.

So, to pull off the Star Trek universal translator thing Meta’s interviewees dreamt about, the Seamless team started with sorting out the data scarcity problem. And they did it in a quite creative way.

Building a universal language

Warren Weaver, a mathematician and pioneer of machine translation, argued in 1949 that there might be a yet undiscovered universal language working as a common base of human communication. This common base of all our communication was exactly what the Seamless team went for in its search for data more than 70 years later. Weaver’s universal language turned out to be math—more precisely, multidimensional vectors.

Machines do not understand words as humans do. To make sense of them, they need to first turn them into sequences of numbers that represent their meaning. Those sequences of numbers are numerical vectors that are termed word embeddings. When you vectorize tens of millions of documents this way, you’ll end up with a huge multidimensional space where words with similar meaning that often go together, like “tea” and “coffee,” are placed close to each other. When you vectorize aligned text in two languages like those European Parliament proceedings, you end up with two separate vector spaces, and then you can run a neural net to learn how those two spaces map onto each other.

But the Meta team didn’t have those nicely aligned texts for all the languages they wanted to cover. So, they vectorized all texts in all languages as if they were just a single language and dumped them into one embedding space called SONAR (Sentence-level Multimodal and Language-Agnostic Representations). Once the text part was done, they went to speech data, which was vectorized using a popular W2v (word to vector) tool and added it to the same massive multilingual, multimodal space. Of course, each embedding carried metadata identifying its source language and whether it was text or speech before vectorization.

The team just used huge amounts of raw data—no fancy human labeling, no human-aligned translations. And then, the data mining magic happened.

SONAR embeddings represented entire sentences instead of single words. Part of the reason behind that was to control for differences between morphologically rich languages, where a single word may correspond to multiple words in morphologically simple languages. But the most important thing was that it ensured that sentences with similar meaning in multiple languages ended up close to each other in the vector space.

It was the same story with speech, too—a spoken sentence in one language was close to spoken sentences in other languages with similar meaning. It even worked between text and speech. So, the team simply assumed that embeddings in two different languages or two different modalities (speech or text) that are at a sufficiently close distance to each other are equivalent to the manually aligned texts of translated documents.

This produced huge amounts of automatically aligned data. The Seamless team suddenly got access to millions of aligned texts, even in low-resource languages, along with thousands of hours of transcribed audio. And they used all this data to train their next-gen translator.

Seamless translation

The automatically generated data set was augmented with human-curated texts and speech samples where possible and used to train multiple AI translation models. The largest one was called SEAMLESSM4T v2. It could translate speech to speech from 101 source languages into any of 36 output languages, and translate text to text. It would also work as an automatic speech recognition system in 96 languages, translate speech to text from 101 into 96 languages, and translate text to speech from 96 into 36 languages—all from a single unified model. It also outperformed state-of-the-art cascading systems by 8 percent in a speech-to-text and by 23 percent in a speech-to-speech translations based on the scores in Bilingual Evaluation Understudy (an algorithm commonly used to evaluate the quality of machine translation).

But it can now do even more than that. The Nature paper published by Meta’s Seamless ends at the SEAMLESSM4T models, but Nature has a long editorial process to ensure scientific accuracy. The paper published on January 15, 2025, was submitted in late November 2023. But in a quick search of the arXiv.org, a repository of not-yet-peer-reviewed papers, you can find the details of two other models that the Seamless team has already integrated on top of the SEAMLESSM4T: SeamlessStreaming and SeamlessExpressive, which take this AI even closer to making a Star Trek universal translator a reality.

SeamlessStreaming is meant to solve the translation latency problem. The baseline SEAMLESSM4T, despite all the bells and whistles, worked as a standard AI translation tool. You had to say what you wanted to say, push “translate,” and it spat out the translation. SeamlessStreaming was designed to take this experience a bit closer to what human simultaneous translator do—it translates what you’re saying as you speak in a streaming fashion. SeamlessExpressive, on the other hand, is aimed at preserving the way you express yourself in translations. When you whisper or say something in a cheerful manner or shout out with anger, SeamlessExpressive will encode the features of your voice, like tone, prosody, volume, tempo, and so on, and transfer those into the output speech in the target language.

Sadly, it still can’t do both at the same time; you can only choose to go for either streaming or expressivity, at least at the moment. Also, the expressivity variant is very limited in supported languages—it only works in English, Spanish, French, and German. But at least it’s online so you can go ahead and give it a spin.

Nature, 2025. DOI: 10.1038/s41586-024-08359-z

Jacek Krywko is a freelance science and technology writer who covers space exploration, artificial intelligence research, computer science, and all sorts of engineering wizardry.

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$getting-an-all-optical-ai-to-handle-non-linear-math$

Getting an all-optical AI to handle non-linear math

AI, Computer science, optical processing, optics, Science / DJ Henderson / January 12, 2025

The problem is that this cascading requires massive parallel computations that, when done on standard computers, take tons of energy and time. Bandyopadhyay’s team feels this problem can be solved by performing the equivalent operations using photons rather than electrons. In photonic chips, information can be encoded in optical properties like polarization, phase, magnitude, frequency, and wavevector. While this would be extremely fast and energy-efficient, building such chips isn’t easy.

Siphoning light

“Conveniently, photonics turned out to be particularly good at linear matrix operations,” Bandyopadhyay claims. A group at MIT led by Dirk Englund, a professor who is a co-author of Bandyopadhyay’s study, demonstrated a photonic chip doing matrix multiplication entirely with light in 2017. What the field struggled with, though, was implementing non-linear functions in photonics.

The usual solution, so far, relied on bypassing the problem by doing linear algebra on photonic chips and offloading non-linear operations to external electronics. This, however, increased latency, since the information had to be converted from light to electrical signals, processed on an external processor, and converted back to light. “And bringing the latency down is the primary reason why we want to build neural networks in photonics,” Bandyopadhyay says.

To solve this problem, Bandyopadhyay and his colleagues designed and built what is likely to be the world’s first chip that can compute the entire deep neural net, including both linear and non-linear operations, using photons. “The process starts with an external laser with a modulator that feeds light into the chip through an optical fiber. This way we convert electrical inputs to light,” Bandyopadhyay explains.

The light is then fanned out to six channels and fed into a layer of six neurons that perform linear matrix multiplication using an array of devices called Mach-Zehnder interferometers. “They are essentially programmable beam splitters, taking two optical fields and mixing them coherently to produce two output optical fields. By applying the voltage, you can control how much those the two inputs mix,” Bandyopadhyay says.

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