Machine-learning decoders for fusion-based quantum computing in real time

arXiv:2611.13630v1 Announce Type: cross Abstract: Fault-tolerant quantum computation requires decoders that combine accuracy, throughput, low latency, and scalability to large numbers of physical qubits. In photonic fusion-based architectures, erasures from photon loss and probabilistic fusion measurements are a dominant error mechanism, motivating decoders that remain accurate at high erasure rates. We introduce PRISM, a transformer-based machine-learning decoder designed to exploit the known locations of erasures in protocols subject to both Pauli and erasure errors. We benchmark PRISM across a wide range of fault-tolerant protocols based on surface codes, color codes, and quantum low-density parity-check codes, with distances up to 24.