Neural decoder for quantum LDPC codes achieves ~10^{-10} logical error at 0.1% physical error with 17x improvement and high throughput, enabling practical fault tolerance at modest code sizes.
arXiv preprint arXiv:1310.0863 , year=
8 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The surface code is designed to suppress errors in quantum computing hardware and currently offers the most believable pathway to large-scale quantum computation. The surface code requires a 2-D array of nearest-neighbor coupled qubits that are capable of implementing a universal set of gates with error rates below approximately 1%, requirements compatible with experimental reality. Consequently, a number of authors are attempting to squeeze additional performance out of the surface code. We describe an optimal complexity error suppression algorithm, parallelizable to O(1) given constant computing resources per unit area, and provide evidence that this algorithm exploits correlations in the error models of each gate in an asymptotically optimal manner.
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representative citing papers
Magic state cultivation prepares high-fidelity T states with an order of magnitude fewer qubit-rounds than prior distillation methods by gradually growing them within a surface code under depolarizing noise.
Extending affine subcode ensemble decoding to quantum codes with overcomplete matrices improves BP convergence and reduces logical error rates on toric and generalized bicycle codes.
The biplanar architecture maps Fermi-Hubbard spin sectors to two planes, eliminating swaps and cutting each Trotter step depth to 4t_synth + 90 logical timesteps versus 6t_synth + 354 in single-plane methods, yielding an estimated 2-hour runtime for L=8 with 1.35 million physical qubits under a 1% 1
FTPrimitiveBench is a new benchmark suite for testing surface-code logical primitives under Pauli-biased, measurement-biased, and spatially non-uniform noise models, revealing that noise structure interacts distinctly with each primitive and decoder.
Adaptive-window decoding that shrinks or expands based on decoder confidence cuts reaction-time overhead in quantum error correction without raising logical error rates.
Convolutional neural network decoders achieve good performance on surface code error correction and adapt across noise models, with explainable AI used to inspect their decisions.
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.
citing papers explorer
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Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation
Neural decoder for quantum LDPC codes achieves ~10^{-10} logical error at 0.1% physical error with 17x improvement and high throughput, enabling practical fault tolerance at modest code sizes.
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Magic state cultivation: growing T states as cheap as CNOT gates
Magic state cultivation prepares high-fidelity T states with an order of magnitude fewer qubit-rounds than prior distillation methods by gradually growing them within a surface code under depolarizing noise.
-
Affine Subcode Ensemble Decoding for Degeneracy-Aware Quantum Error Correction
Extending affine subcode ensemble decoding to quantum codes with overcomplete matrices improves BP convergence and reduces logical error rates on toric and generalized bicycle codes.
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Two Layers, No Swaps: Biplanar SPOQC Architecture Improves Runtime of Fermi-Hubbard Simulation
The biplanar architecture maps Fermi-Hubbard spin sectors to two planes, eliminating swaps and cutting each Trotter step depth to 4t_synth + 90 logical timesteps versus 6t_synth + 354 in single-plane methods, yielding an estimated 2-hour runtime for L=8 with 1.35 million physical qubits under a 1% 1
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FTPrimitiveBench: A Benchmark Suite For Logical Computation Under Hardware-Motivated and Biased Noise Models
FTPrimitiveBench is a new benchmark suite for testing surface-code logical primitives under Pauli-biased, measurement-biased, and spatially non-uniform noise models, revealing that noise structure interacts distinctly with each primitive and decoder.
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ADaPT: Adaptive-window Decoding for Practical fault-Tolerance
Adaptive-window decoding that shrinks or expands based on decoder confidence cuts reaction-time overhead in quantum error correction without raising logical error rates.
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Convolutional neural network based decoders for surface codes
Convolutional neural network decoders achieve good performance on surface code error correction and adapt across noise models, with explainable AI used to inspect their decisions.
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Maximum Likelihood Decoding of Quantum Error Correction Codes
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.