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Error-correcting a quantum computer can mean processing 100TB every second.
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
  • Handling the data
  • What’s next?
Riverlane's founder and CEO, Steve Brierley, told Ars that error correction doesn't only stress the qubit hardware; it stresses the classical portion of the system as well. Each of the measurements of the qubits used for monitoring the system needs to be processed to detect and interpret any errors. We'll need roughly 100 logical qubits to do some of the simplest interesting calculations, meaning monitoring thousands of hardware qubits. Doing more sophisticated calculations may mean thousands of logical qubits.
That error-correction data (termed syndrome data in the field) needs to be read between each operation, which makes for a lot of data. "At scale, we're talking a hundred terabytes per second," said Brierley. "At a million physical qubits, we'll be processing about a hundred terabytes per second, which is Netflix global streaming."
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Both of these versions are highly specialized; they simply feed the error information for other parts of the system to act on. So, it is a highly focused solution.
This kinda reminds me of the ASICS for Bitcoin mining replacing all-round CPUs and GPUs from the early days. Highly specialized hardware for one very specific task seems to be the way to go when solving highly demanding problems.
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