arXiv AI
Jun 30
Neural Minimum Weight Perfect Matching for Quantum Error Codes
arXiv:2601. 00242v2 Announce Type: replace-cross Abstract: Realizing the full potential of quantum computation requires Quantum Error Correction (QEC).
Qubit-centric Transformer for Surface Code Decoding
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arXiv Machine Learning
Jul 8
Latency-Constrained Hardware-Aware Quantum Error Correction Co-Design with Adaptive Confidence-Gated Neural Decoding for the Rotated Surface Code
arXiv:2607. 05814v1 Announce Type: cross Abstract: Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing.
arXiv Machine Learning
Jul 31
QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction
arXiv:2607. 28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale.
arXiv AI
Jun 26
Efficient foundation decoders for fault-tolerant quantum computing
arXiv:2606. 27119v1 Announce Type: cross Abstract: Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficient decoding at large code distances.
arXiv Machine Learning
Aug 18
Machine Learning Approaches to Decoding Topological Quantum Codes
arXiv:2608. 15760v1 Announce Type: cross Abstract: Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information.
arXiv Machine Learning
Sep 3
Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC Codes
The paper introduces QuBA, a Quantum Bayesian graph Attention decoder that provides expressive error-pattern recognition and calibrated uncertainty estimates for quantum error correction. It also presents SAGU, a multi-phase training framework that enhances cross-domain robustness, allowing decoding beyond the training set. Experiments on bivariate bicycle codes show that both QuBA and SAGU outperform classical belief propagation, achieving up to two orders of magnitude lower logical error rates and comparable or better performance than domain-specific training approaches.
arXiv Machine Learning
Jul 8
Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes
arXiv:2607. 05724v1 Announce Type: new Abstract: Quantum convolutional neural networks (QCNNs) combine the power of quantum computing and classical CNN for computational speedup in classification tasks.
arXiv Machine Learning
Sep 4
Learning to Concatenate Quantum Codes
The paper proposes a hybrid, learning‑based strategy for selecting quantum error‑correcting codes at each concatenation level. By estimating the effective noise channel after each level, the method chooses small, non‑additive encoders when the noise has structure and switches to standard codes as the noise becomes uniform. Simulations show that this adaptive approach can achieve a target logical error rate with up to two orders of magnitude fewer qubits than using stabilizer codes alone for strongly structured noise.
arXiv Machine Learning
Jun 26
Tailor Made Embeddings for Quantum Machine Learning
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.
arXiv Machine Learning
Sep 22
Inductive Graph Representation Learning with Quantum Graph Neural Networks
arXiv:2503.24111v4 Announce Type: replace-cross Abstract: Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classica...
arXiv Machine Learning
Sep 3
Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
The paper introduces a new objective function for designing quantum error correction codes that maximizes the distinguishability of quantum states after a noise channel. This approach, called variational quantum error correction (VarQEC), uses a distinguishability loss function as a machine learning objective to discover encoding circuits tailored to specific noise characteristics. The authors demonstrate that VarQEC produces resource‑efficient codes that outperform standard codes and provide proof‑of‑concept experiments on IBM and IQM hardware.
arXiv Machine Learning
Jul 23
Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era
arXiv:2602. 16018v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints.