The weakest part of a modern car’s digital security may sit outside the vehicle itself. A new study finds that more reported weaknesses now involve connected apps, websites and cloud services that handle information about drivers and their journeys. Researchers from the University of Gothenburg and Chalmers University of Technology examined 413 reported automotive software […]

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When a care home suffers a cyberattack, the problem is not limited to computers. Staff may lose access to important records while residents, families and partner organizations urgently need reliable information. A new communications toolkit aims to help adult social care providers prepare for these situations before they happen. Developed by Digital Care Hub with […]

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Running complicated algorithms on quantum computing hardware is going to require the use of logical qubits, where collections of hardware qubits are linked together in ways that allow errors to be detected and corrected. In general, the more hardware qubits you dedicate to each logical qubit, the better the chances are of identifying errors. But, given that the hardware qubits are prone to errors, adding more of them also increases the probability that errors will occur. If the hardware error rate is too high, then growing the logical qubit will only make matters worse.

Fortunately, several companies have demonstrated hardware where the error rate is not too high. Several demonstrations show logical qubits are beyond the breakeven point, where adding more qubits improves performance. The problem is that the improvement isn't very dramatic. To lower error rates to levels we'll need to run complex algorithms, we'd have to add a completely impractical number of qubits to each logical qubit.

On Monday, Quantinuum announced that its current generation of hardware is capable of scaling well beyond the breakeven point. Each step up in the size of logical qubits improved some measures of their performance by a factor of over 10.

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Benchmarks are the rulers of AI, in both senses of the word. As measuring sticks, they work according to an extremely simple logic: If you want to know what an AI model can do, devise a standardized test to gauge it. But AI benchmarks can also dominate the behavior of the entire field. In 1987, the Defense Advanced Research Projects Agency’s first benchmark on automatic speech-to-text…

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