Quantum

FusionQ: A Generic and Optimal Decoding Framework for Ultra-Low-Latency Quantum Error Correction

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Briefings

FusionQ is a generic decoding framework for quantum error correction that derives local tasks from observed syndromes and fuses them into a single global correction. It matches monolithic maximum-likelihood error solutions on every shot while reducing latency by overlapping local decoding with hardware syndrome acquisition. The framework is proven optimal when combined with exact minimum-weight local decoders and boundary-free publication.

Key points

• FusionQ derives local decoding tasks from observed syndrome data rather than using fixed global or windowed models.
• FusionQ matches monolithic maximum-likelihood error solutions on every shot when paired with exact minimum-weight local decoders.

Context

This research addresses quantum error correction decoding by replacing fixed models with dynamic local tasks derived from observed syndromes. The framework achieves optimal global corrections through local fusion, reducing latency by overlapping decoding with hardware acquisition.

Limitations

Evaluation is limited to exact minimum-weight local decoders and boundary-free publication; results may vary with other decoder backends or boundary conditions.

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