Computer science

quantum-computing-progress:-higher-temps,-better-error-correction

Quantum computing progress: Higher temps, better error correction

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

We can fix that —

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

–

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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what-do-threads,-mastodon,-and-hospital-records-have-in-common?

What do Threads, Mastodon, and hospital records have in common?

AI, Computer science, fediverse, health, medicine / /
A medical technician looks at a scan on a computer monitor.

It’s taken a while, but social media platforms now know that people prefer their information kept away from corporate eyes and malevolent algorithms. That’s why the newest generation of social media sites like Threads, Mastodon, and Bluesky boast of being part of the “fediverse.” Here, user data is hosted on independent servers rather than one corporate silo. Platforms then use common standards to share information when needed. If one server starts to host too many harmful accounts, other servers can choose to block it.

They’re not the only ones embracing this approach. Medical researchers think a similar strategy could help them train machine learning to spot disease trends in patients. Putting their AI algorithms on special servers within hospitals for “federated learning” could keep privacy standards high while letting researchers unravel new ways to detect and treat diseases.

“The use of AI is just exploding in all facets of life,” said Ronald M. Summers of the National Institutes of Health Clinical Center in Maryland, who uses the method in his radiology research. “There’s a lot of people interested in using federated learning for a variety of different data analysis applications.”

How does it work?

Until now, medical researchers refined their AI algorithms using a few carefully curated databases, usually anonymized medical information from patients taking part in clinical studies.

However, improving these models further means they need a larger dataset with real-world patient information. Researchers could pool data from several hospitals into one database, but that means asking them to hand over sensitive and highly regulated information. Sending patient information outside a hospital’s firewall is a big risk, so getting permission can be a long and legally complicated process. National privacy laws and the EU’s GDPR law set strict rules on sharing a patient’s personal information.

So instead, medical researchers are sending their AI model to hospitals so it can analyze a dataset while staying within the hospital’s firewall.

Typically, doctors first identify eligible patients for a study, select any clinical data they need for training, confirm its accuracy, and then organize it on a local database. The database is then placed onto a server at the hospital that is linked to the federated learning AI software. Once the software receives instructions from the researchers, it can work its AI magic, training itself with the hospital’s local data to find specific disease trends.

Every so often, this trained model is then sent back to a central server, where it joins models from other hospitals. An aggregation method processes these trained models to update the original model. For example, Google’s popular FedAvg aggregation algorithm takes each element of the trained models’ parameters and creates an average. Each average becomes part of the model update, with their input to the aggregate model weighted proportionally to the size of their training dataset.

In other words, how these models change gets aggregated in the central server to create an updated “consensus model.” This consensus model is then sent back to each hospital’s local database to be trained once again. The cycle continues until researchers judge the final consensus model to be accurate enough. (There’s a review of this process available.)

This keeps both sides happy. For hospitals, it helps preserve privacy since information sent back to the central server is anonymous; personal information never crosses the hospital’s firewall. It also means machine/AI learning can reach its full potential by training on real-world data so researchers get less biased results that are more likely to be sensitive to niche diseases.

Over the past few years, there has been a boom in research using this method. For example, in 2021, Summers and others used federated learning to see whether they could predict diabetes from CT scans of abdomens.

“We found that there were signatures of diabetes on the CT scanner [for] the pancreas that preceded the diagnosis of diabetes by as much as seven years,” said Summers. “That got us very excited that we might be able to help patients that are at risk.”

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if-ai-is-making-the-turing-test-obsolete,-what-might-be-better?

If AI is making the Turing test obsolete, what might be better?

AI, Computer science, Science, Turing test / /
A white android sitting at a table in a depressed manner with an alchoholic drink. Very high resolution 3D render.

If a machine or an AI program matches or surpasses human intelligence, does that mean it can simulate humans perfectly? If yes, then what about reasoning—our ability to apply logic and think rationally before making decisions? How could we even identify whether an AI program can reason? To try to answer this question, a team of researchers has proposed a novel framework that works like a psychological study for software.

“This test treats an ‘intelligent’ program as though it were a participant in a psychological study and has three steps: (a) test the program in a set of experiments examining its inferences, (b) test its understanding of its own way of reasoning, and (c) examine, if possible, the cognitive adequacy of the source code for the program,” the researchers note.

They suggest the standard methods of evaluating a machine’s intelligence, such as the Turing Test, can only tell you if the machine is good at processing information and mimicking human responses. The current generations of AI programs, such as Google’s LaMDA and OpenAI’s ChatGPT, for example, have come close to passing the Turing Test, yet the test results don’t imply these programs can think and reason like humans.

This is why the Turing Test may no longer be relevant, and there is a need for new evaluation methods that could effectively assess the intelligence of machines, according to the researchers. They claim that their framework could be an alternative to the Turing Test. “We propose to replace the Turing test with a more focused and fundamental one to answer the question: do programs reason in the way that humans reason?” the study authors argue.

What’s wrong with the Turing Test?

During the Turing Test, evaluators play different games involving text-based communications with real humans and AI programs (machines or chatbots). It is a blind test, so evaluators don’t know whether they are texting with a human or a chatbot. If the AI programs are successful in generating human-like responses—to the extent that evaluators struggle to distinguish between the human and the AI program—the AI is considered to have passed. However, since the Turing Test is based on subjective interpretation, these results are also subjective.

The researchers suggest that there are several limitations associated with the Turing Test. For instance, any of the games played during the test are imitation games designed to test whether or not a machine can imitate a human. The evaluators make decisions solely based on the language or tone of messages they receive. ChatGPT is great at mimicking human language, even in responses where it gives out incorrect information. So, the test clearly doesn’t evaluate a machine’s reasoning and logical ability.

The results of the Turing Test also can’t tell you if a machine can introspect. We often think about our past actions and reflect on our lives and decisions, a critical ability that prevents us from repeating the same mistakes. The same applies to AI as well, according to a study from Stanford University which suggests that machines that could self-reflect are more practical for human use.

“AI agents that can leverage prior experience and adapt well by efficiently exploring new or changing environments will lead to much more adaptive, flexible technologies, from household robotics to personalized learning tools,” Nick Haber, an assistant professor from Stanford University who was not involved in the current study, said.

In addition to this, the Turing Test fails to analyze an AI program’s ability to think. In a recent Turing Test experiment, GPT-4 was able to convince evaluators that they were texting with humans over 40 percent of the time. However, this score fails to answer the basic question: Can the AI program think?

Alan Turing, the famous British scientist who created the Turing Test, once said, “A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.” His test only covers one aspect of human intelligence, though: imitation. Although it is possible to deceive someone using this one aspect, many experts believe that a machine can never achieve true human intelligence without including those other aspects.

“It’s unclear whether passing the Turing Test is a meaningful milestone or not. It doesn’t tell us anything about what a system can do or understand, anything about whether it has established complex inner monologues or can engage in planning over abstract time horizons, which is key to human intelligence,” Mustafa Suleyman, an AI expert and founder of DeepAI, told Bloomberg.

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