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.