yuewuo/fusion-blossom

Fusion Blossom addresses one of the most critical bottlenecks in fault-tolerant quantum computing: the speed of decoding error signals. Traditional Blossom algorithms are difficult to parallelize; this project implements a 'fusion' approach that allows for distributed decoding across a graph. While its 80 stars suggest a niche audience, in the context of Quantum Error Correction (QEC), this is a significant project associated with peer-reviewed research (Yue Wu and Kenneth Brown). Its primary competitors are Google's 'PyMatching' and 'Stim'. The defensibility lies in the extreme mathematical complexity of implementing a stable, high-performance distributed MWPM solver. However, the moat is limited because the QEC field is rapidly evolving toward neural decoders (ML-based) and specialized hardware decoders (FPGA/ASIC), which may displace software-only MWPM solvers in the 1-2 year horizon as physical qubit counts scale. Frontier labs (OpenAI/Anthropic) have no interest here, but hardware labs (IBM, Google, IonQ) are the primary stakeholders and are likely to develop internal proprietary versions of similar logic.