quantum mechanics

quantum-roundup:-lots-of-companies-announcing-new-tech

Quantum roundup: Lots of companies announcing new tech

Computer science, ibm, IonQ, Physics, Quantum Art, quantum computing, quantum mechanics, Science, trapped ion / /

More superposition, less supposition

IBM follows through on its June promises, plus more trapped ion news.

IBM has moved to large-scale manufacturing of its Quantum Loon chips. Credit: IBM

The end of the year is usually a busy time in the quantum computing arena, as companies often try to announce that they’ve reached major milestones before the year wraps up. This year has been no exception. And while not all of these announcements involve interesting new architectures like the one we looked at recently, they’re a good way to mark progress in the field, and they often involve the sort of smaller, incremental steps needed to push the field forward.

What follows is a quick look at a handful of announcements from the past few weeks that struck us as potentially interesting.

IBM follows through

IBM is one of the companies announcing a brand new architecture this year. That’s not at all a surprise, given that the company promised to do so back in June; this week sees the company confirming that it has built the two processors it said it would earlier in the year. These include one called Loon, which is focused on the architecture that IBM will use to host error-corrected logical qubits. Loon represents two major changes for the company: a shift to nearest-neighbor connections and the addition of long-distance connections.

IBM had previously used what it termed the “heavy hex” architecture, in which alternating qubits were connected to either two or three of their neighbors, forming a set of overlapping hexagonal structures. In Loon, the company is using a square grid, with each qubit having connections to its four closest neighbors. This higher density of connections can enable more efficient use of the qubits during computations. But qubits in Loon have additional long-distance connections to other parts of the chip, which will be needed for the specific type of error correction that IBM has committed to. It’s there to allow users to test out a critical future feature.

The second processor, Nighthawk, is focused on the now. It also has the nearest-neighbor connections and a square grid structure, but it lacks the long-distance connections. Instead, the focus with Nighthawk is to get error rates down so that researchers can start testing algorithms for quantum advantage—computations where quantum computers have a clear edge over classical algorithms.

In addition, the company is launching a GitHub repository that will allow the community to deposit code and performance data for both classical and quantum algorithms, enabling rigorous evaluations of relative performance. Right now, those are broken down into three categories of algorithms that IBM expects are most likely to demonstrate a verifiable quantum advantage.

This isn’t the only follow-up to IBM’s June announcement, which also saw the company describe the algorithm it would use to identify errors in its logical qubits and the corrections needed to fix them. In late October, the company said it had confirmed that the algorithm could work in real time when run on an FPGA made in collaboration with AMD.

Record lows

A few years back, we reported on a company called Oxford Ionics, which had just announced that it achieved a record low error rate in some qubit operations using trapped ions. Most trapped-ion quantum computers move qubits by manipulating electromagnetic fields, but they perform computational operations using lasers. Oxford Ionics figured out how to perform operations using electromagnetic fields, meaning more of their processing benefited from our ability to precisely manufacture circuitry (lasers were still needed for tasks like producing a readout of the qubits). And as we noted, it could perform these computational operations extremely effectively.

But Oxford Ionics never made a major announcement that would give us a good excuse to describe its technology in more detail. The company was ultimately acquired by IonQ, a competitor in the trapped-ion space.

Now, IonQ is building on what it gained from Oxford Ionics, announcing a new, record-low error rate for two-qubit gates: greater than 99.99 percent fidelity. That could be critical for the company, as a low error rate for hardware qubits means fewer are needed to get good performance from error-corrected qubits.

But the details of the two-qubit gates are perhaps more interesting than the error rate. Two-qubit gates involve bringing both qubits involved into close proximity, which often requires moving them. That motion pumps a bit of energy into the system, raising the ions’ temperature and leaving them slightly more prone to errors. As a result, any movement of the ions is generally followed by cooling, in which lasers are used to bleed energy back out of the qubits.

This process, which involves two distinct cooling steps, is slow. So slow that as much as two-thirds of the time spent in operations involves the hardware waiting around while recently moved ions are cooled back down. The new IonQ announcement includes a description of a method for performing two-qubit gates that doesn’t require the ions to be fully cooled. This allows one of the two cooling steps to be skipped entirely. In fact, coupled with earlier work involving one-qubit gates, it raises the possibility that the entire machine could operate with its ions at a still very cold but slightly elevated temperature, avoiding all need for one of the two cooling steps.

That would shorten operation times and let researchers do more before the limit of a quantum system’s coherence is reached.

State of the art?

The last announcement comes from another trapped-ion company, Quantum Art. A couple of weeks back, it announced a collaboration with Nvidia that resulted in a more efficient compiler for operations on its hardware. On its own, this isn’t especially interesting. But it’s emblematic of a trend that’s worth noting, and it gives us an excuse to look at Quantum Art’s technology, which takes a distinct approach to boosting the efficiency of trapped-ion computation.

First, the trend: Nvidia’s interest in quantum computing. The company isn’t interested in the quantum aspects (at least not publicly); instead, it sees an opportunity to get further entrenched in high-performance computing. There are three areas where the computational capacity of GPUs can play a role here. One is small-scale modeling of quantum processors so that users can perform an initial testing of algorithms without committing to paying for access to the real thing. Another is what Quantum Art is announcing: using GPUs as part of a compiler chain to do all the computations needed to find more efficient ways of executing an algorithm on specific quantum hardware.

Finally, there’s a potential role in error correction. Error correction involves some indirect measurements of a handful of hardware qubits to determine the most likely state that a larger collection (called a logical qubit) is in. This requires modeling a quantum system in real time, which is quite difficult—hence the computational demands that Nvidia hopes to meet. Regardless of the precise role, there has been a steady flow of announcements much like Quantum Art’s: a partnership with Nvidia that will keep the company’s hardware involved if the quantum technology takes off.

In Quantum Art’s case, that technology is a bit unusual. The trapped-ion companies we’ve covered so far are all taking different routes to the same place: moving one or two ions into a location where operations can be performed and then executing one- or two-qubit gates. Quantum Art’s approach is to perform gates with much larger collections of ions. At the compiler level, it would be akin to figuring out which qubits need a specific operation performed, clustering them together, and doing it all at once. Obviously, there are potential efficiency gains here.

The challenge would normally be moving so many qubits around to create these clusters. But Quantum Art uses lasers to “pin” ions in a row so they act to isolate the ones to their right from the ones to their left. Each cluster can then be operated on separately. In between operations, the pins can be moved to new locations, creating different clusters for the next set of operations. (Quantum Art is calling each cluster of ions a “core” and presenting this as multicore quantum computing.)

At the moment, Quantum Art is behind some of its competitors in terms of qubit count and performing interesting demonstrations, and it’s not pledging to scale quite as fast. But the company’s founders are convinced that the complexity of doing so many individual operations and moving so many ions around will catch up with those competitors, while the added efficiency of multiple qubit gates will allow it to scale better.

This is just a small sampling of all the announcements from this fall, but it should give you a sense of how rapidly the field is progressing—from technology demonstrations to identifying cases where quantum hardware has a real edge and exploring ways to sustain progress beyond those first successes.

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.

Quantum roundup: Lots of companies announcing new tech Read More »

new-quantum-hardware-puts-the-mechanics-in-quantum-mechanics

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 qubits themselves aren’t manufactured, and there’s no device-to-device among atoms. 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.

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.

New quantum hardware puts the mechanics in quantum mechanics Read More »

google-has-a-useful-quantum-algorithm-that-outperforms-a-supercomputer

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.

Google has a useful quantum algorithm that outperforms a supercomputer Read More »

floating-electrons-on-a-sea-of-helium

Floating electrons on a sea of helium

electrons, liquid helium, Physics, quantum computing, quantum mechanics, qubits, Science / /

By now, a handful of technologies are leading contenders for producing a useful quantum computer. Companies have used them to build machines with dozens to hundreds of qubits, the error rates are coming down, and they’ve largely shifted from worrying about basic scientific problems to dealing with engineering challenges.

Yet even at this apparently late date in the field’s development, there are companies that are still developing entirely new qubit technologies, betting the company that they have identified something that will let them scale in ways that enable a come-from-behind story. Recently, one of those companies published a paper that describes the physics of their qubit system, which involves lone electrons floating on top of liquid helium.

Trapping single electrons

So how do you get an electron to float on top of helium? To find out, Ars spoke with Johannes Pollanen, the chief scientific officer of EeroQ, the company that accomplished the new work. He said that it’s actually old physics, with the first demonstrations of it having been done half a century ago.

“If you bring a charged particle like an electron near the surface, because the helium is dielectric, it’ll create a small image charge underneath in the liquid,” said Pollanen. “A little positive charge, much weaker than the electron charge, but there’ll be a little positive image there. And then the electron will naturally be bound to its own image. It’ll just see that positive charge and kind of want to move toward it, but it can’t get to it, because the helium is completely chemically inert, there are no free spaces for electrons to go.”

Obviously, to get the helium liquid in the first place requires extremely low temperatures. But it can actually remain liquid up to temperatures of 4 Kelvin, which doesn’t require the extreme refrigeration technologies needed for things like transmons. Those temperatures also provide a natural vacuum, since pretty much anything else will also condense out onto the walls of the container.

Diagrams of a chip showing channels and electrodes, along with an image of the chip itself.

The chip itself, along with diagrams of its organization. The trap is set by the gold electrode on the left. Dark channels allow liquid helium and electrons to flow into and out of the trap. And the bluish electrodes at the top and bottom read the presence of the electrons. Credit: EeroQ

Liquid helium is also a superfluid, meaning it flows without viscosity. This allows it to easily flow up tiny channels cut into the surface of silicon chips that the company used for its experiments. A tungsten filament next to the chip was used to load the surface of the helium with electrons at what you might consider the equivalent of a storage basin.

Floating electrons on a sea of helium Read More »

microsoft-lays-out-its-path-to-useful-quantum-computing

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 / /

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.

Microsoft lays out its path to useful quantum computing Read More »

ibm-now-describing-its-first-error-resistant-quantum-compute-system

IBM now describing its first error-resistant quantum compute system

Computer science, computing, ibm, logical qubits, Physics, quantum computing, quantum mechanics, Science / /

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.

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.

IBM now describing its first error-resistant quantum compute system Read More »

startup-puts-a-logical-qubit-in-a-single-piece-of-hardware

Startup puts a logical qubit in a single piece of hardware

Computer science, nord quantique, Physics, quantum computing, quantum mechanics, Science / /

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.

Startup puts a logical qubit in a single piece of hardware Read More »

quantum-hardware-may-be-a-good-match-for-ai

Quantum hardware may be a good match for AI

AI, Computer science, image classification, Physics, quantum computing, quantum mechanics, Science / /

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?

Quantum hardware may be a good match for AI Read More »

microsoft-demonstrates-working-qubits-based-on-exotic-physics

Microsoft demonstrates working qubits based on exotic physics

Apple, Computer science, Majorana, Physics, quantum computing, quantum mechanics, quasiparticles, Science / /

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.”

Microsoft demonstrates working qubits based on exotic physics Read More »

quantum-teleportation-used-to-distribute-a-calculation

Quantum teleportation used to distribute a calculation

Computer science, Physics, quantum computing, quantum mechanics, Science, teleportation / /

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).

Quantum teleportation used to distribute a calculation Read More »

researchers-optimize-simulations-of-molecules-on-quantum-computers

Researchers optimize simulations of molecules on quantum computers

catalysts, Computer science, electrons, Physics, quantum computing, quantum mechanics, Science / /

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).

Researchers optimize simulations of molecules on quantum computers Read More »

google-gets-an-error-corrected-quantum-bit-to-be-stable-for-an-hour

Google gets an error-corrected quantum bit to be stable for an hour

Computer science, Google, logical qubits, Physics, quantum computing, quantum error correction, quantum mechanics, Science / /

Using almost the entire chip for a logical qubit provides long-term stability.

Google’s new Willow chip is its first new generation of chips in about five years. Credit: Google

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

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

A new fab

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

“In the past, all the Sycamore devices that you’ve heard about were fabricated in a shared university clean room space next to graduate students and people doing kinds of crazy stuff,” Google’s Julian Kelly said. “And we’ve made this really significant investment in bringing this new facility online, hiring staff, filling it with tools, transferring their process over. And that enables us to have significantly more process control and dedicated tooling.”

That’s likely to be a critical step for the company, as the ability to fabricate smaller test devices can allow the exploration of lots of ideas on how to structure the hardware to limit the impact of noise. The first publicly announced product of this lab is the Willow processor, Google’s second design, which ups its qubit count to 105. Kelly said one of the changes that came with Willow actually involved making the individual pieces of the qubit larger, which makes them somewhat less susceptible to the influence of noise.

All of that led to a lower error rate, which was critical for the work done in the new paper. This was demonstrated by running Google’s favorite benchmark, one that it acknowledges is contrived in a way to make quantum computing look as good as possible. Still, people have figured out how to make algorithm improvements for classical computers that have kept them mostly competitive. But, with all the improvements, Google expects that the quantum hardware has moved firmly into the lead. “We think that the classical side will never outperform quantum in this benchmark because we’re now looking at something on our new chip that takes under five minutes, would take 1025 years, which is way longer than the age of the Universe,” Kelly said.

Building logical qubits

The work focuses on the behavior of logical qubits, in which a collection of individual hardware qubits are grouped together in a way that enables errors to be detected and corrected. These are going to be essential for running any complex algorithms, since the hardware itself experiences errors often enough to make some inevitable during any complex calculations.

This naturally creates a key milestone. You can get better error correction by adding more hardware qubits to each logical qubit. If each of those hardware qubits produces errors at a sufficient rate, however, then you’ll experience errors faster than you can correct for them. You need to get hardware qubits of a sufficient quality before you start benefitting from larger logical qubits. Google’s earlier hardware had made it past that milestone, but only barely. Adding more hardware qubits to each logical qubit only made for a marginal improvement.

That’s no longer the case. Google’s processors have the hardware qubits laid out on a square grid, with each connected to its nearest neighbors (typically four except at the edges of the grid). And there’s a specific error correction code structure, called the surface code, that fits neatly into this grid. And you can use surface codes of different sizes by using progressively more of the grid. The size of the grid being used is measured by a term called distance, with larger distance meaning a bigger logical qubit, and thus better error correction.

(In addition to a standard surface code, Google includes a few qubits that handle a phenomenon called “leakage,” where a qubit ends up in a higher-energy state, instead of the two low-energy states defined as zero and one.)

The key result is that going from a distance of three to a distance of five more than doubled the ability of the system to catch and correct errors. Going from a distance of five to a distance of seven doubled it again. Which shows that the hardware qubits have reached a sufficient quality that putting more of them into a logical qubit has an exponential effect.

“As we increase the grid from three by three to five by five to seven by seven, the error rate is going down by a factor of two each time,” said Google’s Michael Newman. “And that’s that exponential error suppression that we want.”

Going big

The second thing they demonstrated is that, if you make the largest logical qubit that the hardware can support, with a distance of 15, it’s possible to hang onto the quantum information for an average of an hour. This is striking because Google’s earlier work had found that its processors experience widespread simultaneous errors that the team ascribed to cosmic ray impacts. (IBM, however, has indicated it doesn’t see anything similar, so it’s not clear whether this diagnosis is correct.) Those happened every 10 seconds or so. But this work shows that a sufficiently large error code can correct for these events, whatever their cause.

That said, these qubits don’t survive indefinitely. One of them seems to be a localized temporary increase in errors. The second, more difficult to deal with problem involves a widespread spike in error detection affecting an area that includes roughly 30 qubits. At this point, however, Google has only seen six of these events, so they told Ars that it’s difficult to really characterize them. “It’s so rare it actually starts to become a bit challenging to study because you have to gain a lot of statistics to even see those events at all,” said Kelly.

Beyond the relative durability of these logical qubits, the paper notes another advantage to going with larger code distances: it enhances the impact of further hardware improvements. Google estimates that at a distance of 15, improving hardware performance by a factor of two would drop errors in the logical qubit by a factor of 250. At a distance of 27, the same hardware improvement would lead to an improvement of over 10,000 in the logical qubit’s performance.

Note that none of this will ever get the error rate to zero. Instead, we just need to get the error rate to a level where an error is unlikely for a given calculation (more complex calculations will require a lower error rate). “It’s worth understanding that there’s always going to be some type of error floor and you just have to push it low enough to the point where it practically is irrelevant,” Kelly said. “So for example, we could get hit by an asteroid and the entire Earth could explode and that would be a correlated error that our quantum computer is not currently built to be robust to.”

Obviously, a lot of additional work will need to be done to both make logical qubits like this survive for even longer, and to ensure we have the hardware to host enough logical qubits to perform calculations. But the exponential improvements here, to Google, suggest that there’s nothing obvious standing in the way of that. “We woke up one morning and we kind of got these results and we were like, wow, this is going to work,” Newman said. “This is really it.”

Nature, 2024. DOI: 10.1038/s41586-024-08449-y  (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.

Google gets an error-corrected quantum bit to be stable for an hour Read More »