arXiv Machine Learning

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

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.

By Ran Miao, Rui Luo, Xiaohan Shan, Xiaoming Sun

fine-tuning benchmarks

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).

By Yotam Peled, David Zenati, Eliya Nachmani

llms

arXiv Machine Learning

Jun 3

Discovering autonomous quantum error correction via deep reinforcement learning

arXiv:2511. 12482v2 Announce Type: replace-cross Abstract: Quantum error correction is essential for fault-tolerant quantum computing.

By Yue Yin, Tailong Xiao, Xiaoyang Deng, Ming He, Jianping Fan, Guihua Zeng

agents reinforcement-learning

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.

By Aldo Lamarre, Dominik \v{S}afr\'anek

rag

arXiv AI

Jul 2

When AI meets quantum information: A comprehensive review

arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.

By Min Chen, Yu Gan, Xin Jin, Yuqing Li, Junqi Wang, Zeguan Wu, Yunfei Wang, Bingzhi Zhang, Priyam Srivastava, Tianlong Chen, Ankit Kulshrestha, Yuan Liu, Juan Jos\'e Mendoza-Arenas, Kaushik P. Seshadreesan, Sarvagya Upadhyay, Xueyue Zhang, Quntao Zhuang, Junyu Liu

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.

By Ge Yan, Shanchuan Li, Shiyi Xiao, Pengyue Ma, Hanyan Cao, Feng Pan, Yuxuan Du

llms

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.

By Sumit Chongder

efficiency benchmarks

arXiv Machine Learning

Jul 30

Shot-based quantum encoding: a data-loading paradigm for quantum neural networks

arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.

By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov

rag benchmarks

arXiv Machine Learning

Jul 23

Machine-learned syndrome post-selection for reliable quantum error correction

arXiv:2607. 19563v1 Announce Type: cross Abstract: Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information.

By Tobias Haug, Askery Canabarro, Leandro Aolita

efficiency

arXiv Machine Learning

Jun 9

Quantum feature-map learning with reduced resource overhead

arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.

By Jonas J\"ager, Philipp Els\"asser, Elham Torabian

efficiency benchmarks

arXiv Machine Learning

Aug 4

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications

arXiv:2608. 01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues.

By L\'eo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin, Pavel Sekatski, Viktoria Patapovich, Asel Sagingalieva, Alexey Melnikov

benchmarks

arXiv Machine Learning

Jun 9

Quantum Global Variational Learning for Quantum Error Correction

arXiv:2606. 08592v1 Announce Type: new Abstract: Efficient quantum error correction is essential for the advancement of quantum computing.

By Shun Ryuzaki, Hideo Mukai

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