Sparse Mamba Decoder for Quantum Error Correction: Efficient Defect-Centric Processing of Surface Code Syndromes
arXiv:2605.17156v2 Announce Type: replace-cross Abstract: Quantum error correction (QEC) is essential for building fault-tolerant quantum computers, requiring decoders that are simultaneously accurate, fast, and scalable. Most state-of-the-art neural decoders achieve high accuracy but process the full dense syndrome array of size $O(d^2 R) $regardless of the actual error rate, where d is the code distance and R is the number of measurement rounds. At physically relevant error rates (p ~ 0.1%), fewer than 5% of syndrome entries contain active detection events -- yet existing decoders process th
Advances in neural network architectures and quantum computing research are converging to address critical challenges in quantum error correction more efficiently.
Efficient quantum error correction decoders are crucial for building fault-tolerant quantum computers, directly impacting the feasibility and scalability of quantum computing technology.
The development of sparse Mamba decoders offers a path to significantly more efficient processing of quantum error correction syndromes, reducing computational overhead without sacrificing accuracy.
- · Quantum computing hardware manufacturers
- · Applied quantum computing researchers
- · AI/ML for scientific computing
- · Traditional dense syndrome decoding methods
- · Companies reliant on less efficient QEC methods
Increased efficiency in quantum error correction reduces the overhead for building large-scale quantum computers.
Faster and more accurate QEC accelerates the timeline for achieving fault-tolerant quantum computation and practical quantum advantage.
The enhanced practicality of quantum computing could catalyze breakthroughs in materials science, drug discovery, and cryptography, leading to new industries and economic shifts.
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