Nvidia · Google · IEEE Spectrum AI
The Classical Pushes Needed to Make Quantum Computers Tick
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Key facts
- Other companies, including IBM Quantum, Cambridge, UK-based which develops quantum-error correction company Riverlane, and Google Quantum AI, are developing similar tools
- Google is therefore developing a hardware architecture that can incorporate both traditional and AI-based decoders, including its AlphaQubit 2 model
- But offloading to a GPU still introduces significant latency, says Marco Ghibaudi, VP of engineering at Riverlane
- More complex errors are passed to a traditional algorithmic decoder, but the first pass reduces computational load enough to deliver a 2x speed-up
Summary
Nvidia and others are developing new software, hardware, and AI to enable quantum. Quantum computers promise to one day solve problems beyond the most powerful supercomputers imaginable. To prepare for the scale of quantum computers the industry is working towards, many companies are also gearing up the classical hardware, and software, required to support them. Digital computer chips are marvels of engineering, operating flawlessly out of the box and capable of trillions of operations without error. Calibration and error-correction are fundamentally classical, not quantum, problems, and they require dedicated classical hardware to solve.