quantum computing

Google identifies low noise “phase transition” in its quantum processor

Computer science, error rate, Google, NISQ, quantum computing, quantum mechanics, Science / 9u50fv / October 9, 2024

Noisy, but not that noisy

Benchmark may help us understand how quantum computers can operate with low error.

Image of a chip above iridescent wiring.

Google’s Sycamore processor. Credit: Google

Back in 2019, Google made waves by claiming it had achieved what has been called “quantum supremacy”—the ability of a quantum computer to perform operations that would take a wildly impractical amount of time to simulate on standard computing hardware. That claim proved to be controversial, in that the operations were little more than a benchmark that involved getting the quantum computer to behave like a quantum computer; separately, improved ideas about how to perform the simulation on a supercomputer cut the time required down significantly.

But Google is back with a new exploration of the benchmark, described in a paper published in Nature on Wednesday. It uses the benchmark to identify what it calls a phase transition in the performance of its quantum processor and uses it to identify conditions where the processor can operate with low noise. Taking advantage of that, they again show that, even giving classical hardware every potential advantage, it would take a supercomputer a dozen years to simulate things.

Cross entropy benchmarking

The benchmark in question involves the performance of what are called quantum random circuits, which involves performing a set of operations on qubits and letting the state of the system evolve over time, so that the output depends heavily on the stochastic nature of measurement outcomes in quantum mechanics. Each qubit will have a probability of producing one of two results, but unless that probability is one, there’s no way of knowing which of the results you’ll actually get. As a result, the output of the operations will be a string of truly random bits.

If enough qubits are involved in the operations, then it becomes increasingly difficult to simulate the performance of a quantum random circuit on classical hardware. That difficulty is what Google originally used to claim quantum supremacy.

The big challenge with running quantum random circuits on today’s hardware is the inevitability of errors. And there’s a specific approach, called cross-entropy benchmarking, that relates the performance of quantum random circuits to the overall fidelity of the hardware (meaning its ability to perform error-free operations).

Google Principal Scientist Sergio Boixo likened performing quantum random circuits to a race between trying to build the circuit and errors that would destroy it. “In essence, this is a competition between quantum correlations spreading because you’re entangling, and random circuits entangle as fast as possible,” he told Ars. “We use two qubit gates that entangle as fast as possible. So it’s a competition between correlations or entanglement growing as fast as you want. On the other hand, noise is doing the opposite. Noise is killing correlations, it’s killing the growth of correlations. So these are the two tendencies.”

The focus of the paper is using the cross-entropy benchmark to explore the errors that occur on the company’s latest generation of Sycamore chip and use that to identify the transition point between situations where errors dominate, and what the paper terms a “low noise regime,” where the probability of errors are minimized—where entanglement wins the race. The researchers likened this to a phase transition between two states.

Low noise performance

The researchers used a number of methods to identify the location of this phase transition, including numerical estimates of the system’s behavior and experiments using the Sycamore processor. Boixo explained that the transition point is related to the errors per cycle, with each cycle involving performing an operation on all of the qubits involved. So, the total number of qubits being used influences the location of the transition, since more qubits means more operations to perform. But so does the overall error rate on the processor.

If you want to operate in the low noise regime, then you have to limit the number of qubits involved (which has the side effect of making things easier to simulate on classical hardware). The only way to add more qubits is to lower the error rate. While the Sycamore processor itself had a well-understood minimal error rate, Google could artificially increase that error rate and then gradually lower it to explore Sycamore’s behavior at the transition point.

The low noise regime wasn’t error free; each operation still has the potential for error, and qubits will sometimes lose their state even when sitting around doing nothing. But this error rate could be estimated using the cross-entropy benchmark to explore the system’s overall fidelity. That wasn’t the case beyond the transition point, where errors occurred quickly enough that they would interrupt the entanglement process.

When this occurs, the result is often two separate, smaller entangled systems, each of which were subject to the Sycamore chip’s base error rates. The researchers simulated this by creating two distinct clusters of entangled qubits that could be entangled with each other by a single operation, allowing them to turn entanglement on and off at will. They showed that this behavior allowed a classical computer to spoof the overall behavior by breaking the computation up into two manageable chunks.

Ultimately, they used their characterization of the phase transition to identify the maximum number of qubits they could keep in the low noise regime given the Sycamore processor’s base error rate and then performed a million random circuits on them. While this is relatively easy to do on quantum hardware, even assuming that we could build a supercomputer without bandwidth constraints, simulating it would take roughly 10,000 years on an existing supercomputer (the Frontier system). Allowing all of the system’s storage to operate as secondary memory cut the estimate down to 12 years.

What does this tell us?

Boixo emphasized that the value of the work isn’t really based on the value of performing random quantum circuits. Truly random bit strings might be useful in some contexts, but he emphasized that the real benefit here is a better understanding of the noise level that can be tolerated in quantum algorithms more generally. Since this benchmark is designed to make it as easy as possible to outperform classical computations, you would need the best standard computers here to have any hope of beating them to the answer for more complicated problems.

“Before you can do any other application, you need to win on this benchmark,” Boixo said. “If you are not winning on this benchmark, then you’re not winning on any other benchmark. This is the easiest thing for a noisy quantum computer compared to a supercomputer.”

Knowing how to identify this phase transition, he suggested, will also be helpful for anyone trying to run useful computations on today’s processors. “As we define the phase, it opens the possibility for finding applications in that phase on noisy quantum computers, where they will outperform classical computers,” Boixo said.

Implicit in this argument is an indication of why Google has focused on iterating on a single processor design even as many of its competitors have been pushing to increase qubit counts rapidly. If this benchmark indicates that you can’t get all of Sycamore’s qubits involved in the simplest low-noise regime calculation, then it’s not clear whether there’s a lot of value in increasing the qubit count. And the only way to change that is to lower the base error rate of the processor, so that’s where the company’s focus has been.

All of that, however, assumes that you hope to run useful calculations on today’s noisy hardware qubits. The alternative is to use error-corrected logical qubits, which will require major increases in qubit count. But Google has been seeing similar limitations due to Sycamore’s base error rate in tests that used it to host an error-corrected logical qubit, something we hope to return to in future coverage.

Nature, 2024. DOI: 10.1038/s41586-024-07998-6 (About DOIs).

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 opens its quantum-computing stack to third parties

Computer science, ibm, qiskit, quantum computing, quantum mechanics, Science, Software / Ryan Harris / September 28, 2024

Image of a large collection of copper-colored metal plates and wires, all surrounding a small, black piece of silicon. — Enlarge / The small quantum processor (center) surrounded by cables that carry microwave signals to it, and the refrigeration hardware.

As we described earlier this year, operating a quantum computer will require a significant investment in classical computing resources, given the amount of measurements and control operations that need to be executed and interpreted. That means that operating a quantum computer will also require a software stack to control and interpret the flow of information from the quantum side.

But software also gets involved well before anything gets executed. While it’s possible to execute algorithms on quantum hardware by defining the full set of commands sent to the hardware, most users are going to want to focus on algorithm development, rather than the details of controlling any single piece of quantum hardware. “If everyone’s got to get down and know what the noise is, [use] performance management tools, they’ve got to know how to compile a quantum circuit through hardware, you’ve got to become an expert in too much to be able to do the algorithm discovery,” said IBM’s Jay Gambetta. So, part of the software stack that companies are developing to control their quantum hardware includes software that converts abstract representations of quantum algorithms into the series of commands needed to execute them.

IBM’s version of this software is called Qiskit (although it was made open source and has since been adopted by other companies). Recently, IBM made a couple of announcements regarding Qiskit, both benchmarking it in comparison to other software stacks and opening it up to third-party modules. We’ll take a look at what software stacks do before getting into the details of what’s new.

What’s the software stack do?

It’s tempting to view IBM’s Qiskit as the equivalent of a compiler. And at the most basic level, that’s a reasonable analogy, in that it takes algorithms defined by humans and converts them to things that can be executed by hardware. But there are significant differences in the details. A compiler for a classical computer produces code that the computer’s processor converts to internal instructions that are used to configure the processor hardware and execute operations.

Even when using what’s termed “machine language,” programmers don’t directly control the hardware; programmers have no control over where on the hardware things are executed (ie, which processor or execution unit within that processor), or even the order instructions are executed in.

Things are very different for quantum computers, at least at present. For starters, everything that happens on the processor is controlled by external hardware, which typically act by generating a series of laser or microwave pulses. So, software like IBM’s Qiskit or Microsoft’s Q# act by converting the code they’re given into commands that are sent to hardware that’s external to the processor.

These “compilers” must also keep track of exactly which part of the processor things are happening on. Quantum computers act by performing specific operations (called gates) on individual or pairs of qubits; to do that, you have to know exactly which qubit you’re addressing. And, for things like superconducting qubits, where there can be device-to-device variations, which hardware qubits you end up using can have a significant effect on the outcome of the calculations.

As a result, most things like Qiskit provide the option of directly addressing the hardware. If a programmer chooses not to, however, the software can transform generic instructions into a precise series of actions that will execute whatever algorithm has been encoded. That involves the software stack making choices about which physical qubits to use, what gates and measurements to execute, and what order to execute them in.

The role of the software stack, however, is likely to expand considerably over the next few years. A number of companies are experimenting with hardware qubit designs that can flag when one type of common error occurs, and there has been progress with developing logical qubits that enable error correction. Ultimately, any company providing access to quantum computers will want to modify its software stack so that these features are enabled without requiring effort on the part of the people designing the algorithms.

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Microsoft performs operations with multiple error-corrected qubits

atom computing, Error correction, microsoft, quantinuum, quantum computing, qubits, Science / Mike M. / September 10, 2024

Image of a chip with a device on it that is shaped like two triangles connected by a bar. — Enlarge / Quantinuum’s H2 “racetrack” quantum processor.

Quantinuum

On Tuesday, Microsoft made a series of announcements related to its Azure Quantum Cloud service. Among them was a demonstration of logical operations using the largest number of error-corrected qubits yet.

“Since April, we’ve tripled the number of logical qubits here,” said Microsoft Technical Fellow Krysta Svore. “So we are accelerating toward that hundred-logical-qubit capability.” The company has also lined up a new partner in the form of Atom Computing, which uses neutral atoms to hold qubits and has already demonstrated hardware with over 1,000 hardware qubits.

Collectively, the announcements are the latest sign that quantum computing has emerged from its infancy and is rapidly progressing toward the development of systems that can reliably perform calculations that would be impractical or impossible to run on classical hardware. We talked with people at Microsoft and some of its hardware partners to get a sense of what’s coming next to bring us closer to useful quantum computing.

Making error correction simpler

Logical qubits are a route out of the general despair of realizing that we’re never going to keep hardware qubits from producing too many errors for reliable calculation. Error correction on classical computers involves measuring the state of bits and comparing their values to an aggregated value. Unfortunately, you can’t analogously measure the state of a qubit to determine if an error has occurred since measurement causes it to adopt a concrete value, destroying any of the superposition of values that make quantum computing useful.

Logical qubits get around this by spreading a single bit of quantum information across a collection of bits, which makes any error less catastrophic. Detecting when one occurs involves adding some additional bits to the logical qubit such that their value is dependent upon the ones holding the data. You can measure these ancillary qubits to identify if any problem has occurred and possibly gain information on how to correct it.

There are many potential error correction schemes, some of which can involve dedicating around a thousand qubits to each logical qubit. It’s possible to get away with far less than that—schemes with fewer than 10 qubits exist. But in general, the fewer hardware qubits you use, the greater your chance of experiencing errors that you can’t recover from. This trend can be offset in part through hardware qubits that are less error-prone.

The challenge is that this only works if error rates are low enough that you don’t run into errors during the correction process. In other words, the hardware qubits have to be good enough that they don’t produce so many errors that it’s impossible to know when an error has occurred and how to correct it. That threshold has been passed only relatively recently.

Microsoft’s earlier demonstration involved the use of hardware from Quantinuum, which uses qubits based on ions trapped in electrical fields. These have some of the best error rates yet reported, and Microsoft had shown that this allowed it to catch and correct errors over several rounds of error correction. In the new work, the collaboration went further, performing multiple logical operations with error correction on a collection of logical qubits.

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Why every quantum computer will need a powerful classical computer

computing, Error correction, quantum computing, qubits, Science, startups / Ari B / July 9, 2024

Image of a set of spheres with arrows within them, with all the arrows pointing in the same direction. — Enlarge / A single logical qubit is built from a large collection of hardware qubits.

One of the more striking things about quantum computing is that the field, despite not having proven itself especially useful, has already spawned a collection of startups that are focused on building something other than qubits. It might be easy to dismiss this as opportunism—trying to cash in on the hype surrounding quantum computing. But it can be useful to look at the things these startups are targeting, because they can be an indication of hard problems in quantum computing that haven’t yet been solved by any one of the big companies involved in that space—companies like Amazon, Google, IBM, or Intel.

In the case of a UK-based company called Riverlane, the unsolved piece that is being addressed is the huge amount of classical computations that are going to be necessary to make the quantum hardware work. Specifically, it’s targeting the huge amount of data processing that will be needed for a key part of quantum error correction: recognizing when an error has occurred.

Error detection vs. the data

All qubits are fragile, tending to lose their state during operations, or simply over time. No matter what the technology—cold atoms, superconducting transmons, whatever—these error rates put a hard limit on the amount of computation that can be done before an error is inevitable. That rules out doing almost every useful computation operating directly on existing hardware qubits.

The generally accepted solution to this is to work with what are called logical qubits. These involve linking multiple hardware qubits together and spreading the quantum information among them. Additional hardware qubits are linked in so that they can be measured to monitor errors affecting the data, allowing them to be corrected. It can take dozens of hardware qubits to make a single logical qubit, meaning even the largest existing systems can only support about 50 robust logical qubits.

Riverlane’s founder and CEO, Steve Brierley, told Ars that error correction doesn’t only stress the qubit hardware; it stresses the classical portion of the system as well. Each of the measurements of the qubits used for monitoring the system needs to be processed to detect and interpret any errors. We’ll need roughly 100 logical qubits to do some of the simplest interesting calculations, meaning monitoring thousands of hardware qubits. Doing more sophisticated calculations may mean thousands of logical qubits.

That error-correction data (termed syndrome data in the field) needs to be read between each operation, which makes for a lot of data. “At scale, we’re talking a hundred terabytes per second,” said Brierley. “At a million physical qubits, we’ll be processing about a hundred terabytes per second, which is Netflix global streaming.”

It also has to be processed in real time, otherwise computations will get held up waiting for error correction to happen. To avoid that, errors must be detected in real time. For transmon-based qubits, syndrome data is generated roughly every microsecond, so real time means completing the processing of the data—possibly Terabytes of it—with a frequency of around a Megahertz. And Riverlane was founded to provide hardware that’s capable of handling it.

Handling the data

The system the company has developed is described in a paper that it has posted on the arXiv. It’s designed to handle syndrome data after other hardware has already converted the analog signals into digital form. This allows Riverlane’s hardware to sit outside any low-temperature hardware that’s needed for some forms of physical qubits.

That data is run through an algorithm the paper terms a “Collision Clustering decoder,” which handles the error detection. To demonstrate its effectiveness, they implement it based on a typical Field Programmable Gate Array from Xilinx, where it occupies only about 5 percent of the chip but can handle a logical qubit built from nearly 900 hardware qubits (simulated, in this case).

The company also demonstrated a custom chip that handled an even larger logical qubit, while only occupying a tiny fraction of a square millimeter and consuming just 8 milliwatts of power.

Both of these versions are highly specialized; they simply feed the error information for other parts of the system to act on. So, it is a highly focused solution. But it’s also quite flexible in that it works with various error-correction codes. Critically, it also integrates with systems designed to control a qubit based on very different physics, including cold atoms, trapped ions, and transmons.

“I think early on it was a bit of a puzzle,” Brierley said. “You’ve got all these different types of physics; how are we going to do this?” It turned out not to be a major challenge. “One of our engineers was in Oxford working with the superconducting qubits, and in the afternoon he was working with the iron trap qubits. He came back to Cambridge and he was all excited. He was like, ‘They’re using the same control electronics.'” It turns out that, regardless of the physics involved in controlling the qubits, everybody had borrowed the same hardware from a different field (Brierley said it was a Xilinx radiofrequency system-on-a-chip built for 5G base stationed prototyping.) That makes it relatively easy to integrate Riverlane’s custom hardware with a variety of systems.

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Quantum computing progress: Higher temps, better error correction

Amazon, Computer science, ibm, Physics, quantum computing, quantum mechanics, Science / Ari B / March 27, 2024

conceptual graphic of symbols representing quantum states floating above a stylized computer chip.

There’s a strong consensus that tackling most useful problems with a quantum computer will require that the computer be capable of error correction. There is absolutely no consensus, however, about what technology will allow us to get there. A large number of companies, including major players like Microsoft, Intel, Amazon, and IBM, have all committed to different technologies to get there, while a collection of startups are exploring an even wider range of potential solutions.

We probably won’t have a clearer picture of what’s likely to work for a few years. But there’s going to be lots of interesting research and development work between now and then, some of which may ultimately represent key milestones in the development of quantum computing. To give you a sense of that work, we’re going to look at three papers that were published within the last couple of weeks, each of which tackles a different aspect of quantum computing technology.

Hot stuff

Error correction will require connecting multiple hardware qubits to act as a single unit termed a logical qubit. This spreads a single bit of quantum information across multiple hardware qubits, making it more robust. Additional qubits are used to monitor the behavior of the ones holding the data and perform corrections as needed. Some error correction schemes require over a hundred hardware qubits for each logical qubit, meaning we’d need tens of thousands of hardware qubits before we could do anything practical.

A number of companies have looked at that problem and decided we already know how to create hardware on that scale—just look at any silicon chip. So, if we could etch useful qubits through the same processes we use to make current processors, then scaling wouldn’t be an issue. Typically, this has meant fabricating quantum dots on the surface of silicon chips and using these to store single electrons that can hold a qubit in their spin. The rest of the chip holds more traditional circuitry that performs the initiation, control, and readout of the qubit.

This creates a notable problem. Like many other qubit technologies, quantum dots need to be kept below one Kelvin in order to keep the environment from interfering with the qubit. And, as anyone who’s ever owned an x86-based laptop knows, all the other circuitry on the silicon generates heat. So, there’s the very real prospect that trying to control the qubits will raise the temperature to the point that the qubits can’t hold onto their state.

That might not be the problem that we thought, according to some work published in Wednesday’s Nature. A large international team that includes people from the startup Diraq have shown that a silicon quantum dot processor can work well at the relatively toasty temperature of 1 Kelvin, up from the usual milliKelvin that these processors normally operate at.

The work was done on a two-qubit prototype made with materials that were specifically chosen to improve noise tolerance; the experimental procedure was also optimized to limit errors. The team then performed normal operations starting at 0.1 K, and gradually ramped up the temperatures to 1.5 K, checking performance as they did so. They found that a major source of errors, state preparation and measurement (SPAM), didn’t change dramatically in this temperature range: “SPAM around 1 K is comparable to that at millikelvin temperatures and remains workable at least until 1.4 K.”

The error rates they did see depended on the state they were preparing. One particular state (both spin-up) had a fidelity of over 99 percent, while the rest were less constrained, at somewhere above 95 percent. States had a lifetime of over a millisecond, which qualifies as long-lived int he quantum world.

All of which is pretty good, and suggests that the chips can tolerate reasonable operating temperatures, meaning on-chip control circuitry can be used without causing problems. The error rates of the hardware qubits are still well above those that would be needed for error correction to work. However, the researchers suggest that they’ve identified error processes that can potentially be compensated for. They expect that the ability to do industrial-scale manufacturing will ultimately lead to working hardware.

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iMessage gets a major makeover that puts it on equal footing with Signal

Apple, Biz & IT, iMessage, messaging apps, MESSENGER, quantum computing, Security, signal / Blake Jones / February 22, 2024

iMessage is getting a major makeover that makes it among the two messaging apps most prepared to withstand the coming advent of quantum computing, largely at parity with Signal or arguably incrementally more hardened.

On Wednesday, Apple said messages sent through iMessage will now be protected by two forms of end-to-end encryption (E2EE), whereas before, it had only one. The encryption being added, known as PQ3, is an implementation of a new algorithm called Kyber that, unlike the algorithms iMessage has used until now, can’t be broken with quantum computing. Apple isn’t replacing the older quantum-vulnerable algorithm with PQ3—it’s augmenting it. That means, for the encryption to be broken, an attacker will have to crack both.

Making E2EE future safe

The iMessage changes come five months after the Signal Foundation, maker of the Signal Protocol that encrypts messages sent by more than a billion people, updated the open standard so that it, too, is ready for post-quantum computing (PQC). Just like Apple, Signal added Kyber to X3DH, the algorithm it was using previously. Together, they’re known as PQXDH.

iMessage and Signal provide end-to-end encryption, a protection that makes it impossible for anyone other than the sender and recipient of a message to read it in decrypted form. iMessage began offering E2EE with its rollout in 2011. Signal became available in 2014.

One of the biggest looming threats to many forms of encryption is quantum computing. The strength of the algorithms used in virtually all messaging apps relies on mathematical problems that are easy to solve in one direction and extremely hard to solve in the other. Unlike a traditional computer, a quantum computer with sufficient resources can solve these problems in considerably less time.

No one knows how soon that day will come. One common estimate is that a quantum computer with 20 million qubits (a basic unit of measurement) will be able to crack a single 2,048-bit RSA key in about eight hours. The biggest known quantum computer to date has 433 qubits.

Whenever that future arrives, cryptography engineers know it’s inevitable. They also know that it’s likely some adversaries will collect and stockpile as much encrypted data now and decrypt it once quantum advances allow for it. The moves by both Apple and Signal aim to defend against that eventuality using Kyber, one of several PQC algorithms currently endorsed by the National Institute of Standards and Technology. Since Kyber is still relatively new, both iMessage and Signal will continue using the more tested algorithms for the time being.

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Alternate qubit design does error correction in hardware

Computer science, photons, Physics, quantum computing, quantum mechanics, Science / Mike M. / February 11, 2024

We can fix that —

Early-stage technology has the potential to cut qubits needed for useful computers.

John Timmer – Feb 9, 2024 5: 57 pm UTC

Image of a complicated set of wires and cables hooked up to copper colored metal hardware. — Nord Quantique

There’s a general consensus that performing any sort of complex algorithm on quantum hardware will have to wait for the arrival of error-corrected qubits. Individual qubits are too error-prone to be trusted for complex calculations, so quantum information will need to be distributed across multiple qubits, allowing monitoring for errors and intervention when they occur.

But most ways of making these “logical qubits” needed for error correction require anywhere from dozens to over a hundred individual hardware qubits. This means we’ll need anywhere from tens of thousands to millions of hardware qubits to do calculations. Existing hardware has only cleared the 1,000-qubit mark within the last month, so that future appears to be several years off at best.

But on Thursday, a company called Nord Quantique announced that it had demonstrated error correction using a single qubit with a distinct hardware design. While this has the potential to greatly reduce the number of hardware qubits needed for useful error correction, the demonstration involved a single qubit—the company doesn’t even expect to demonstrate operations on pairs of qubits until later this year.

Meet the bosonic qubit

The technology underlying this work is termed a bosonic qubit, and they’re not anything new; an optical instrument company even has a product listing for them that notes their potential for use in error correction. But while the concepts behind using them in this manner were well established, demonstrations were lagging. Nord Quantique has now posted a paper in the arXiv that details a demonstration of them actually lowering error rates.

The devices are structured much like a transmon, the form of qubit favored by tech heavyweights like IBM and Google. There, the quantum information is stored in a loop of superconducting wire and is controlled by what’s called a microwave resonator—a small bit of material where microwave photons will reflect back and forth for a while before being lost.

A bosonic qubit turns that situation on its head. In this hardware, the quantum information is held in the photons, while the superconducting wire and resonator control the system. These are both hooked up to a coaxial cavity (think of a structure that, while microscopic, looks a bit like the end of a cable connector).

Massively simplified, the quantum information is stored in the manner in which the photons in the cavity interact. The state of the photons can be monitored by the linked resonator/superconducting wire. If something appears to be off, the resonator/superconducting wire allows interventions to be made to restore the original state. Additional qubits are not needed. “A very simple and basic idea behind quantum error correction is redundancy,” co-founder and CTO Julien Camirand Lemyre told Ars. “One thing about resonators and oscillators in superconducting circuits is that you can put a lot of photons inside the resonators. And for us, the redundancy comes from there.”

This process doesn’t correct all possible errors, so it doesn’t eliminate the need for logical qubits made from multiple underlying hardware qubits. In theory, though, you can catch the two most common forms of errors that qubits are prone to (bit flips and changes in phase).

In the arXiv preprint, the team at Nord Quantique demonstrated that the system works. Using a single qubit and simply measuring whether it holds onto its original state, the error correction system can reduce problems by 14 percent. Unfortunately, overall fidelity is also low, starting at about 85 percent, which is significantly below what’s seen in other systems that have been through years of development work. Some qubits have been demonstrated with a fidelity of over 99 percent.

Getting competitive

So there’s no question that Nord Quantique is well behind a number of the leaders in quantum computing that can perform (error-prone) calculations with dozens of qubits and have far lower error rates. Again, Nord Quantique’s work was done using a single qubit—and without doing any of the operations needed to perform a calculation.

Lemyre told Ars that while the company is small, it benefits from being a spin-out of the Institut Quantique at Canada’s Sherbrooke University, one of Canada’s leading quantum research centers. In addition to having access to the expertise there, Nord Quantique uses a fabrication facility at Sherbrooke to make its hardware.

Over the next year, the company expects to demonstrate that the error correction scheme can function while pairs of qubits are used to perform gate operations, the fundamental units of calculations. Another high priority is to combine this hardware-based error correction with more traditional logical qubit schemes, which would allow additional types of errors to be caught and corrected. This would involve operations with a dozen or more of these bosonic qubits at a time.

But the real challenge will be in the longer term. The company is counting on its hardware’s ability to handle error correction to reduce the number of qubits needed for useful calculations. But if its competitors can scale up the number of qubits fast enough while maintaining the control and error rates needed, that may not ultimately matter. Put differently, if Nord Quantique is still in the hundreds of qubit range by the time other companies are in the hundreds of thousands, its technology might not succeed even if it has some inherent advantages.

But that’s the fun part about the field as things stand: We don’t really know. A handful of very different technologies are already well into development and show some promise. And there are other sets that are still early in the development process but are thought to have a smoother path to scaling to useful numbers of qubits. All of them will have to scale to a minimum of tens of thousands of qubits while enabling the ability to perform quantum manipulations that were cutting-edge science just a few decades ago.

Looming in the background is the simple fact that we’ve never tried to scale anything like this to the extent that will be needed. Unforeseen technical hurdles might limit progress at some point in the future.

Despite all this, there are people backing each of these technologies who know far more about quantum mechanics than I ever will. It’s a fun time.

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