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INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY

ADAPTIVE REINFORCEMENT LEARNING FOR QUANTUM ERROR CORRECTION UNDER NON-STATIONARY NOISE: A CONTINUAL/META-RL FRAMEWORK FOR DRIFT-ROBUST DECODING

P. Suresh Varma, M. V. Rama Sundari

Published Oct 5, 2026• 0 citations
Publisher

Abstract

Quantum error correction (QEC) decoders are usually trained and evaluated assuming stationary physical noise.Real superconducting and trapped-ion processors, however, exhibit noise that drifts over calibration cycles, shifts abruptly because of twolevel-system defects and cosmic-ray events, and varies across qubits.Static decoders, whether algorithmic (minimum-weight perfect matching, union-find) or learned (such as AlphaQubit), degrade when deployed noise diverges from the training distribution, and full retraining is costly and can cause catastrophic forgetting.This paper proposes Adaptive-RL-QEC, a research framework that formulates decoding and noise adaptation as a reinforcement learning problem over a non-stationary Markov decision process with an unobserved noise-regime variable.It couples a lightweight online noise-regime estimator with a continual/meta-reinforcement-learning decoding policy that adapts within a bounded number of syndrome rounds without catastrophic P.

DOI
10.34218/ijcet_17_05_007

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Quantum error correction (QEC) decoders are usually trained and evaluated assuming stationary physical noise.Real superconducting and trapped-ion processors, however, exhibit noise that drifts over calibration cycles, shifts abruptly because of twolevel-system defects and cosmic-ray events, and varies across qubits.Static decoders, whether algorithmic (minimum-weight perfect matching, union-find) or learned (such as AlphaQubit), degrade when deployed noise diverges from the training distribution, and full retraining is costly and can cause catastrophic forgetting.This paper proposes Adaptive-RL-QEC, a research framework that formulates decoding and noise adaptation as a reinforcement learning problem over a non-stationary Markov decision process with an unobserved noise-regime variable.It couples a lightweight online noise-regime estimator with a continual/meta-reinforcement-learning decoding policy that adapts within a bounded number of syndrome rounds without catastrophic P.
Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment. Its prerequisite is that errors must remain sufficiently rare, which requires perpetually adapting the computer's control parameters to the drifting environment conditions. The current solution to this problem is to terminate the entire quantum computation for recalibration, but it is incompatible with the long runtimes of future quantum algorithms. We address this challenge by unifying calibration with computation. We grant the QEC process a dual role: its error detection events are not only used to correct the logical quantum state, but are also repurposed as a learning signal, teaching a reinforcement learning (RL) agent to continuously steer the control parameters and stabilize the quantum system during computation. We experimentally demonstrate this framework on a Willow superconducting processor, improving the logical stability of the surface code 3.5-fold against injected drift. By synthesizing our full suite of technological advances, we achieve record performance of the surface and color codes, with average logical error per cycle of 7.72(9)×10−47.72(9)\times10^{-4}7.72(9)×10−4 and 8.19(14)×10−38.19(14)\times10^{-3}8.19(14)×10−3 respectively. Numerical simulations of large codes with tens of thousands of control parameters confirm the scalability of our RL framework, revealing an optimization speed that is independent of system size. This work thus enables a new paradigm: a quantum computer that learns from its errors and never stops computing.
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.
In realistic hardware for quantum computation that possesses fault-tolerance, non-stationary noise and stochastic drift lead to logical failure from the temporal accumulation of errors, not from independent events. Static decoding and fixed calibration techniques are structurally incompatible with this situation because they do not take into account temporal correlations between errors or control-induced back-action of errors. These effects motivate control policies that must track noise evolution across correction cycles, rather than respond to individual syndromes in isolation. We treat fault-tolerant quantum computation as a stochastic control problem, modelled using reduced quantum dynamics in which Pauli error processes are governed by latent noise parameters that vary temporally. From this perspective, logical failure arises through the accumulation of a hazard variable, and the corresponding control objective depends on the full history of observations. Operating under these conditions, a Chronological Deep Q-Network (Ch-DQN) maintains an internal belief state that tracks both noise evolution and accumulated hazard. During training, backward refinement of trajectories is used to sample slowly drifting modes of operation, while runtime inference remains strictly causal. A fractional meta-update stabilizes learning in the presence of non-stationary, control-coupled dynamics. Through multi-distance simulations that incorporate stochastic drift and feedback from decision-making, Ch-DQN suppresses hazard accumulation and extends logical survival time relative to static and recurrent baselines. Error correction in this regime is therefore no longer a static decoding task, but a control process whose success is determined over time by the underlying noise dynamics.
With a particular emphasis on fixing fault defects caused by bitflip and depolarizing noise, this study explores the use of RL methods within the framework of quantum error correction. This work investigates the flexibility and independence of RL-trained agents in reducing bitflip mistakes by methodically looking at error rates and qubit lifetimes. Furthermore, we study how the top-performing agents are affected by depolarizing noise and present a strategic "Decoded Surface Code" method for fixing mistakes. The results show how RL works in handling different types of errors on its own, revealing subtle dynamics in the quantum system. Quantum error correction is a rapidly developing area, and this study adds to it by shedding light on how reinforcement learning could improve quantum computing's fault tolerance. The study's findings provide a basis for future research into more robust quantum computing systems and have ramifications for the creation of autonomous error-correcting algorithms.
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.
Quantum error correction (QEC) is a crucial element in the realization of fault-tolerant quantum computation. However, traditional QEC schemes often rely on pre-determined, static parameters, which may not be optimally suited for the specific noise characteristics present in a given quantum hardware. This work introduces a novel approach to QEC that leverages reinforcement learning (RL) to dynamically adapt the error correction protocol in real-time. The system learns to optimize the parameters of the QEC code, such as measurement schedules and decoding strategies, based on continuous feedback from the quantum hardware. This adaptive approach promises to significantly improve the performance and efficiency of QEC, reducing the overhead associated with error correction and enhancing the fidelity of quantum computations. The core claim of this paper is that quantum error correction is computationally intensive and requires precise control over quantum systems. The central mechanism involves employing reinforcement learning to optimize the parameters of quantum error correction protocols in real-time, adapting to the specific noise characteristics of the quantum hardware. This represents a new automation of the QEC process.