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.

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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.

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There's a general consensus that we won't be able to consistently perform sophisticated quantum calculations without the development of error-corrected quantum computing, which is unlikely to arrive until the end of the decade. It's still an open question, however, whether we could perform limited but useful calculations at an earlier point. IBM is one of the companies that's betting the answer is yes, and on Wednesday, it announced a series of developments aimed at making that possible.

On their own, none of the changes being announced are revolutionary. But collectively, changes across the hardware and software stacks have produced much more efficient and less error-prone operations. The net result is a system that supports the most complicated calculations yet on IBM's hardware, leaving the company optimistic that its users will find some calculations where quantum hardware provides an advantage.

Better hardware and software

IBM's early efforts in the quantum computing space saw it ramp up the qubit count rapidly, being one of the first companies to reach the 1,000 qubit count. However, each of those qubits had an error rate that ensured that any algorithms that tried to use all of these qubits in a single calculation would inevitably trigger one. Since then, the company's focus has been on improving the performance of smaller processors. Wednesday's announcement was based on the introduction of the second version of its Heron processor, which has 156 qubits (up from an earlier 133 in Revision 1). That's still beyond the capability of simulations on classical computers, should it be able to operate with sufficiently low errors.

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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.

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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.

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Enlarge / Quantinuum's H2 "racetrack" quantum processor. (credit: 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.

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Enlarge / A single logical qubit is built from a large collection of hardware qubits. (credit: at digit)

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.

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