IBM uses an AI agent factory to speed up error correction decoder design 52x

[HardyBoy_WCW]

IBM Research has used AI agents to speed up the hardware design of a quantum error correction decoder, and Quantum Zeitgeist has written up the results from IBM's blog. The approach uses what IBM calls an agent factory, where a coordinating AI agent directs many worker agents running in parallel. It is a neat example of AI helping to build quantum computers

The target was the Relay-BP decoder for IBM's [[144,12,12]] bivariate bicycle code, the qLDPC code at the heart of its fault tolerance plans. The agents explored 100 design hypotheses for each configuration, looking for ways to make the decoder hardware faster

The results are interesting on cost as well as speed. A setup using open weight frontier models, served through IBM's open source llm-d inference framework, achieved a 52 times speedup in high level synthesis estimates for about $26 of inference. It used around 241 million tokens at a blended cost of roughly $0.11 per million, helped by a 98 percent prefix cache hit rate

A setup using only proprietary frontier models reached a bigger 204 times speedup, but cost $299, about 11.5 times more. A hybrid configuration was also tested. IBM is careful to note that each configuration ran only once, so the results are not a firm ranking of the models. IBM Fellow Ruchir Puri is quoted in the post

Decoders need to be extremely fast to keep up with quantum chips, so optimising their hardware matters a lot. Using AI agents to explore designs cheaply and quickly could speed up the engineering side of quantum computing considerably. It is also a nice demonstration of how far open models have come

Is AI assisted chip design going to become standard for quantum hardware? And does the cost difference between open and proprietary models surprise you?

IBM Achieves 52x Design Speedup With AI Agents Tackling Quantum Error Correctionquantumzeitgeist.com