quantum mechanics

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 / Tim Belzer / December 9, 2024

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).

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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Qubit that makes most errors obvious now available to customers

Computer science, dual-rail qubits, quantum computing, quantum mechanics, qubits, Science / Kris Guyer / November 20, 2024

Can a small machine that makes error correction easier upend the market?

A graphic representation of the two resonance cavities that can hold photons, along with a channel that lets the photon move between them. Credit: Quantum Circuits

We’re nearing the end of the year, and there are typically a flood of announcements regarding quantum computers around now, in part because some companies want to live up to promised schedules. Most of these involve evolutionary improvements on previous generations of hardware. But this year, we have something new: the first company to market with a new qubit technology.

The technology is called a dual-rail qubit, and it is intended to make the most common form of error trivially easy to detect in hardware, thus making error correction far more efficient. And, while tech giant Amazon has been experimenting with them, a startup called Quantum Circuits is the first to give the public access to dual-rail qubits via a cloud service.

While the tech is interesting on its own, it also provides us with a window into how the field as a whole is thinking about getting error-corrected quantum computing to work.

What’s a dual-rail qubit?

Dual-rail qubits are variants of the hardware used in transmons, the qubits favored by companies like Google and IBM. The basic hardware unit links a loop of superconducting wire to a tiny cavity that allows microwave photons to resonate. This setup allows the presence of microwave photons in the resonator to influence the behavior of the current in the wire and vice versa. In a transmon, microwave photons are used to control the current. But there are other companies that have hardware that does the reverse, controlling the state of the photons by altering the current.

Dual-rail qubits use two of these systems linked together, allowing photons to move from the resonator to the other. Using the superconducting loops, it’s possible to control the probability that a photon will end up in the left or right resonator. The actual location of the photon will remain unknown until it’s measured, allowing the system as a whole to hold a single bit of quantum information—a qubit.

This has an obvious disadvantage: You have to build twice as much hardware for the same number of qubits. So why bother? Because the vast majority of errors involve the loss of the photon, and that’s easily detected. “It’s about 90 percent or more [of the errors],” said Quantum Circuits’ Andrei Petrenko. “So it’s a huge advantage that we have with photon loss over other errors. And that’s actually what makes the error correction a lot more efficient: The fact that photon losses are by far the dominant error.”

Petrenko said that, without doing a measurement that would disrupt the storage of the qubit, it’s possible to determine if there is an odd number of photons in the hardware. If that isn’t the case, you know an error has occurred—most likely a photon loss (gains of photons are rare but do occur). For simple algorithms, this would be a signal to simply start over.

But it does not eliminate the need for error correction if we want to do more complex computations that can’t make it to completion without encountering an error. There’s still the remaining 10 percent of errors, which are primarily something called a phase flip that is distinct to quantum systems. Bit flips are even more rare in dual-rail setups. Finally, simply knowing that a photon was lost doesn’t tell you everything you need to know to fix the problem; error-correction measurements of other parts of the logical qubit are still needed to fix any problems.

The layout of the new machine. Each qubit (gray square) involves a left and right resonance chamber (blue dots) that a photon can move between. Each of the qubits has connections that allow entanglement with its nearest neighbors. Credit: Quantum Circuits

In fact, the initial hardware that’s being made available is too small to even approach useful computations. Instead, Quantum Circuits chose to link eight qubits with nearest-neighbor connections in order to allow it to host a single logical qubit that enables error correction. Put differently: this machine is meant to enable people to learn how to use the unique features of dual-rail qubits to improve error correction.

One consequence of having this distinctive hardware is that the software stack that controls operations needs to take advantage of its error detection capabilities. None of the other hardware on the market can be directly queried to determine whether it has encountered an error. So, Quantum Circuits has had to develop its own software stack to allow users to actually benefit from dual-rail qubits. Petrenko said that the company also chose to provide access to its hardware via its own cloud service because it wanted to connect directly with the early adopters in order to better understand their needs and expectations.

Numbers or noise?

Given that a number of companies have already released multiple revisions of their quantum hardware and have scaled them into hundreds of individual qubits, it may seem a bit strange to see a company enter the market now with a machine that has just a handful of qubits. But amazingly, Quantum Circuits isn’t alone in planning a relatively late entry into the market with hardware that only hosts a few qubits.

Having talked with several of them, there is a logic to what they’re doing. What follows is my attempt to convey that logic in a general form, without focusing on any single company’s case.

Everyone agrees that the future of quantum computation is error correction, which requires linking together multiple hardware qubits into a single unit termed a logical qubit. To get really robust, error-free performance, you have two choices. One is to devote lots of hardware qubits to the logical qubit, so you can handle multiple errors at once. Or you can lower the error rate of the hardware, so that you can get a logical qubit with equivalent performance while using fewer hardware qubits. (The two options aren’t mutually exclusive, and everyone will need to do a bit of both.)

The two options pose very different challenges. Improving the hardware error rate means diving into the physics of individual qubits and the hardware that controls them. In other words, getting lasers that have fewer of the inevitable fluctuations in frequency and energy. Or figuring out how to manufacture loops of superconducting wire with fewer defects or handle stray charges on the surface of electronics. These are relatively hard problems.

By contrast, scaling qubit count largely involves being able to consistently do something you already know how to do. So, if you already know how to make good superconducting wire, you simply need to make a few thousand instances of that wire instead of a few dozen. The electronics that will trap an atom can be made in a way that will make it easier to make them thousands of times. These are mostly engineering problems, and generally of similar complexity to problems we’ve already solved to make the electronics revolution happen.

In other words, within limits, scaling is a much easier problem to solve than errors. It’s still going to be extremely difficult to get the millions of hardware qubits we’d need to error correct complex algorithms on today’s hardware. But if we can get the error rate down a bit, we can use smaller logical qubits and might only need 10,000 hardware qubits, which will be more approachable.

Errors first

And there’s evidence that even the early entries in quantum computing have reasoned the same way. Google has been working iterations of the same chip design since its 2019 quantum supremacy announcement, focusing on understanding the errors that occur on improved versions of that chip. IBM made hitting the 1,000 qubit mark a major goal but has since been focused on reducing the error rate in smaller processors. Someone at a quantum computing startup once told us it would be trivial to trap more atoms in its hardware and boost the qubit count, but there wasn’t much point in doing so given the error rates of the qubits on the then-current generation machine.

The new companies entering this market now are making the argument that they have a technology that will either radically reduce the error rate or make handling the errors that do occur much easier. Quantum Circuits clearly falls into the latter category, as dual-rail qubits are entirely about making the most common form of error trivial to detect. The former category includes companies like Oxford Ionics, which has indicated it can perform single-qubit gates with a fidelity of over 99.9991 percent. Or Alice & Bob, which stores qubits in the behavior of multiple photons in a single resonance cavity, making them very robust to the loss of individual photons.

These companies are betting that they have distinct technology that will let them handle error rate issues more effectively than established players. That will lower the total scaling they need to do, and scaling will be an easier problem overall—and one that they may already have the pieces in place to handle. Quantum Circuits’ Petrenko, for example, told Ars, “I think that we’re at the point where we’ve gone through a number of iterations of this qubit architecture where we’ve de-risked a number of the engineering roadblocks.” And Oxford Ionics told us that if they could make the electronics they use to trap ions in their hardware once, it would be easy to mass manufacture them.

None of this should imply that these companies will have it easy compared to a startup that already has experience with both reducing errors and scaling, or a giant like Google or IBM that has the resources to do both. But it does explain why, even at this stage in quantum computing’s development, we’re still seeing startups enter the field.

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Microsoft and Atom Computing combine for quantum error correction demo

atom computing, Computer science, microsoft, Physics, quantum computing, quantum error correction, quantum mechanics, Science / Paul Patrick / November 19, 2024

New work provides a good view of where the field currently stands.

The first-generation tech demo of Atom’s hardware. Things have progressed considerably since. Credit: Atom Computing

In September, Microsoft made an unusual combination of announcements. It demonstrated progress with quantum error correction, something that will be needed for the technology to move much beyond the interesting demo phase, using hardware from a quantum computing startup called Quantinuum. At the same time, however, the company also announced that it was forming a partnership with a different startup, Atom Computing, which uses a different technology to make qubits available for computations.

Given that, it was probably inevitable that the folks in Redmond, Washington, would want to show that similar error correction techniques would also work with Atom Computing’s hardware. It didn’t take long, as the two companies are releasing a draft manuscript describing their work on error correction today. The paper serves as both a good summary of where things currently stand in the world of error correction, as well as a good look at some of the distinct features of computation using neutral atoms.

Atoms and errors

While we have various technologies that provide a way of storing and manipulating bits of quantum information, none of them can be operated error-free. At present, errors make it difficult to perform even the simplest computations that are clearly beyond the capabilities of classical computers. More sophisticated algorithms would inevitably encounter an error before they could be completed, a situation that would remain true even if we could somehow improve the hardware error rates of qubits by a factor of 1,000—something we’re unlikely to ever be able to do.

The solution to this is to use what are called logical qubits, which distribute quantum information across multiple hardware qubits and allow the detection and correction of errors when they occur. Since multiple qubits get linked together to operate as a single logical unit, the hardware error rate still matters. If it’s too high, then adding more hardware qubits just means that errors will pop up faster than they can possibly be corrected.

We’re now at the point where, for a number of technologies, hardware error rates have passed the break-even point, and adding more hardware qubits can lower the error rate of a logical qubit based on them. This was demonstrated using neutral atom qubits by an academic lab at Harvard University about a year ago. The new manuscript demonstrates that it also works on a commercial machine from Atom Computing.

Neutral atoms, which can be held in place using a lattice of laser light, have a number of distinct advantages when it comes to quantum computing. Every single atom will behave identically, meaning that you don’t have to manage the device-to-device variability that’s inevitable with fabricated electronic qubits. Atoms can also be moved around, allowing any atom to be entangled with any other. This any-to-any connectivity can enable more efficient algorithms and error-correction schemes. The quantum information is typically stored in the spin of the atom’s nucleus, which is shielded from environmental influences by the cloud of electrons that surround it, making them relatively long-lived qubits.

Operations, including gates and readout, are performed using lasers. The way the physics works, the spacing of the atoms determines how the laser affects them. If two atoms are a critical distance apart, the laser can perform a single operation, called a two-qubit gate, that affects both of their states. Anywhere outside this distance, and a laser only affects each atom individually. This allows a fine control over gate operations.

That said, operations are relatively slow compared to some electronic qubits, and atoms can occasionally be lost entirely. The optical traps that hold atoms in place are also contingent upon the atom being in its ground state; if any atom ends up stuck in a different state, it will be able to drift off and be lost. This is actually somewhat useful, in that it converts an unexpected state into a clear error.

Image of a grid of dots arranged in sets of parallel vertical rows. There is a red bar across the top, and a green bar near the bottom of the grid. — Atom Computing’s system. Rows of atoms are held far enough apart so that a single laser sent across them (green bar) only operates on individual atoms. If the atoms are moved to the interaction zone (red bar), a laser can perform gates on pairs of atoms. Spaces where atoms can be held can be left empty to avoid performing unneeded operations. Credit: Reichardt, et al.

The machine used in the new demonstration hosts 256 of these neutral atoms. Atom Computing has them arranged in sets of parallel rows, with space in between to let the atoms be shuffled around. For single-qubit gates, it’s possible to shine a laser across the rows, causing every atom it touches to undergo that operation. For two-qubit gates, pairs of atoms get moved to the end of the row and moved a specific distance apart, at which point a laser will cause the gate to be performed on every pair present.

Atom’s hardware also allows a constant supply of new atoms to be brought in to replace any that are lost. It’s also possible to image the atom array in between operations to determine whether any atoms have been lost and if any are in the wrong state.

It’s only logical

As a general rule, the more hardware qubits you dedicate to each logical qubit, the more simultaneous errors you can identify. This identification can enable two ways of handling the error. In the first, you simply discard any calculation with an error and start over. In the second, you can use information about the error to try to fix it, although the repair involves additional operations that can potentially trigger a separate error.

For this work, the Microsoft/Atom team used relatively small logical qubits (meaning they used very few hardware qubits), which meant they could fit more of them within 256 total hardware qubits the machine made available. They also checked the error rate of both error detection with discard and error detection with correction.

The research team did two main demonstrations. One was placing 24 of these logical qubits into what’s called a cat state, named after Schrödinger’s hypothetical feline. This is when a quantum object simultaneously has non-zero probability of being in two mutually exclusive states. In this case, the researchers placed 24 logical qubits in an entangled cat state, the largest ensemble of this sort yet created. Separately, they implemented what’s called the Bernstein-Vazirani algorithm. The classical version of this algorithm requires individual queries to identify each bit in a string of them; the quantum version obtains the entire string with a single query, so is a notable case of something where a quantum speedup is possible.

Both of these showed a similar pattern. When done directly on the hardware, with each qubit being a single atom, there was an appreciable error rate. By detecting errors and discarding those calculations where they occurred, it was possible to significantly improve the error rate of the remaining calculations. Note that this doesn’t eliminate errors, as it’s possible for multiple errors to occur simultaneously, altering the value of the qubit without leaving an indication that can be spotted with these small logical qubits.

Discarding has its limits; as calculations become increasingly complex, involving more qubits or operations, it will inevitably mean every calculation will have an error, so you’d end up wanting to discard everything. Which is why we’ll ultimately need to correct the errors.

In these experiments, however, the process of correcting the error—taking an entirely new atom and setting it into the appropriate state—was also error-prone. So, while it could be done, it ended up having an overall error rate that was intermediate between the approach of catching and discarding errors and the rate when operations were done directly on the hardware.

In the end, the current hardware has an error rate that’s good enough that error correction actually improves the probability that a set of operations can be performed without producing an error. But not good enough that we can perform the sort of complex operations that would lead quantum computers to have an advantage in useful calculations. And that’s not just true for Atom’s hardware; similar things can be said for other error-correction demonstrations done on different machines.

There are two ways to go beyond these current limits. One is simply to improve the error rates of the hardware qubits further, as fewer total errors make it more likely that we can catch and correct them. The second is to increase the qubit counts so that we can host larger, more robust logical qubits. We’re obviously going to need to do both, and Atom’s partnership with Microsoft was formed in the hope that it will help both companies get there faster.

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IBM boosts the amount of computation you can get done on quantum hardware

Biz & IT, Computer science, computing quantum computing, ibm, Physics, quantum mechanics, Science / Tim Belzer / November 13, 2024

By making small adjustments to the frequency that the qubits are operating at, it’s possible to avoid these problems. This can be done when the Heron chip is being calibrated before it’s opened for general use.

Separately, the company has done a rewrite of the software that controls the system during operations. “After learning from the community, seeing how to run larger circuits, [we were able to] almost better define what it should be and rewrite the whole stack towards that,” Gambetta said. The result is a dramatic speed-up. “Something that took 122 hours now is down to a couple of hours,” he told Ars.

Since people are paying for time on this hardware, that’s good for customers now. However, it could also pay off in the longer run, as some errors can occur randomly, so less time spent on a calculation can mean fewer errors.

Deeper computations

Despite all those improvements, errors are still likely during any significant calculations. While it continues to work toward developing error-corrected qubits, IBM is focusing on what it calls error mitigation, which it first detailed last year. As we described it then:

“The researchers turned to a method where they intentionally amplified and then measured the processor’s noise at different levels. These measurements are used to estimate a function that produces similar output to the actual measurements. That function can then have its noise set to zero to produce an estimate of what the processor would do without any noise at all.”

The problem here is that using the function is computationally difficult, and the difficulty increases with the qubit count. So, while it’s still easier to do error mitigation calculations than simulate the quantum computer’s behavior on the same hardware, there’s still the risk of it becoming computationally intractable. But IBM has also taken the time to optimize that, too. “They’ve got algorithmic improvements, and the method that uses tensor methods [now] uses the GPU,” Gambetta told Ars. “So I think it’s a combination of both.”

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

Computer science, error rate, Google, NISQ, quantum computing, quantum mechanics, Science / Tim Belzer / 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).

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IBM opens its quantum-computing stack to third parties

Computer science, ibm, qiskit, quantum computing, quantum mechanics, Science, Software / Rejus Almole / 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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Quantum computing progress: Higher temps, better error correction

Amazon, Computer science, ibm, Physics, quantum computing, quantum mechanics, Science / Kris Guyer / 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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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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