Asia and South Pacific Design Automation Conference (ASP-DAC)

Reinforcement Learning for Enhanced Advanced QEC Architecture Decoding

Yidong Zhou, Lingyi Kong, Yifeng Peng, Zhiding Liang

Published Jan 19, 2026• 0 citations

DOI: 10.1109/asp-dac66049.2026.11420491

Related papers

Yidong Zhou, Lingyi Kong, Yifeng Peng +1 more

The advent of promising quantum error correction (QEC) codes with efficient resource utilization and high-performance fault-tolerant quantum memories signifies a critical step towards realizing practical quantum computation. While surface codes have been a dominant approach, their limitations have spurred the development of more advanced QEC architectures. These advanced codes often present increased complexity, demanding innovative decoding methodologies. This work investigates the application of reinforcement learning (RL) techniques, including hybrid and multi-agent approaches, to enhance the decoding of various advanced QEC architectures. By leveraging the ability of RL to learn optimal strategies from noisy syndrome measurements, we explore the potential for achieving improved logical error rates and scalability compared to traditional decoding methods. Our approach examines the adaptation of reinforcement learning to exploit the structural properties of these modern QEC models. We also explore the benefits of combining different RL algorithms to address the multifaceted nature of the decoding problem, considering factors such as code degeneracy and real-world noise characteristics. With our proposed method, we are able to demonstrate that an autonomously trained agent can derive decoding schemes for the complex decoding requirement of advanced QEC architectures.

quant-pharXiv:2601.19279Jan 28, 20260 citations

Quantum Reinforcement Learning

Daoyi Dong, Chunlin Chen, Zonghai Chen

Lecture notes in computer scienceJan 1, 200544 citations

Quantum Reinforcement Learning: Concepts, Models, and Applications

Joongheon Kim

Lecture notes on data engineering and communications technologiesJan 1, 20240 citations

Chrysalis-QEC: A Model-Free Single-Shot Quantum Decoder via Noise-Agnostic Meta-Reinforcement Learning

Yassin Gaffar

Quantum Error Correction (QEC) is indispensable for protecting fragile quantum states against environmental decoherence and hardware imperfections. Traditionally, the Minimum Weight Perfect Matching (MWPM) algorithm has served as the benchmark decoder; however, its heavy geometric calculations introduce significant processing latencies and require strict, pre-calibrated prior knowledge of physical error distributions. Deep learning decoders have emerged to address these constraints but commonly suffer from catastrophic forgetting when operational environments dynamically fluctuate. To resolve these challenges, this work introduces Chrysalis-QEC, a novel model-free Meta-Reinforcement Learning (Meta-RL) decoder optimized for real-time, low-latency, single-shot quantum error correction. Implemented via a customized Gymnasium environment integrated with the Stim simulation library, the architecture employs a sequential curriculum learning framework driven by Proximal Policy Optimization (PPO). By progressively transferring policy parameters across escalating noise baselines (from 5% to 7% and 10%), the agent maps the underlying topological structures of error syndromes rather than memorizing static error probabilities. Under dynamic shock testing featuring stochastic noise variations jumping between 1% and 10%, Chrysalis-QEC achieves decoding success rates of 94.00% for distance-3 and 95.00% for distance-5 configurations. The proposed framework demonstrates superior stability, significantly lower decoding variance, and instantaneous adaptation compared to the baseline MWPM decoder, validating its efficacy as a noise-agnostic solution for real-world fault-tolerant quantum computing architectures.

Aug 16, 20260 citations

Adaptive Co-Design of Quantum Machine Learning Algorithms and Error Correction Protocols using Reinforcement Learning

Stephane Herman Maes

The convergence of quantum computing and artificial intelligence presents profound opportunities but faces significant hurdles, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum Machine Learning (QML) algorithms, while promising, exhibit sensitivity to noise and scalability challenges, hindering the demonstration of practical quantum advantage. Concurrently, Quantum Error Correction (QEC), essential for fault tolerance, imposes substantial resource overheads and is often developed generically, without specific adaptation to the target application's error sensitivity. This paper reviews the current state of the intersection of AI and quantum computing, examining both QML paradigms, e.g., Variational Quantum Algorithms, Quantum Kernels, and the burgeoning use of AI to enhance quantum computing itself, e.g., quantum control, QEC decoding, circuit design. A critical gap identified is the lack of frameworks that systematically co-design QML algorithms and QEC protocols adaptively. To address this, a novel framework is proposed based on Reinforcement Learning (RL). This framework employs an RL agent to dynamically adjust both the QML circuit architecture, e.g., VQC ansatz, and QEC parameters, e.g., decoding strategy, measurement frequency, based on observed application performance and estimated error characteristics. This adaptive co-design loop aims to optimize the trade-off between QML performance and QEC overhead, enhancing noise resilience and resource efficiency. The potential advantages, feasibility, and limitations of this approach are discussed, alongside with future research directions aimed at realizing robust and practical Quantum AI.

May 16, 20252 citations

A reinforcement learning approach for quantum state engineering

Jelena Mackeprang, Durga Bhaktavatsala Rao Dasari, Jörg Wrachtrup

arXiv:1908.05981Quantum Machine IntelligenceMay 11, 202052 citations