Reinforcement Learning for Enhanced Advanced QEC Architecture Decoding

• URL: http://arxiv.org/abs/2601.19279v1
• Date: Tue, 27 Jan 2026 07:08:03 GMT
• Title: Reinforcement Learning for Enhanced Advanced QEC Architecture Decoding
• Abstract summary: This work investigates the application of reinforcement learning techniques, including hybrid and multi-agent approaches, to enhance the decoding of advanced QEC architectures.<n>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.
• Score: 4.159020226352125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Deep Unfolding: Recent Developments, Theory, and Design Guidelines [99.63555420898554]
  This article provides a tutorial-style overview of deep unfolding, a framework that transforms optimization algorithms into structured, trainable ML architectures.<n>We review the foundations of optimization for inference and for learning, introduce four representative design paradigms for deep unfolding, and discuss the distinctive training schemes that arise from their iterative nature.
  arXiv  Detail & Related papers  (2025-12-03T13:16:35Z)
• Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities [54.12860202362483]
  This article comprehensively reviews recent developments and research on deep learning-based (DL-based) techniques for integrated sensing and communication (ISAC) systems.<n>ISAC is regarded as a key enabler for 6G and beyond networks, as many emerging applications, such as vehicular networks and industrial robotics, necessitate both sensing and communication capabilities.<n>As an alternative to conventional techniques, DL-based techniques offer efficient and near-optimal solutions with reduced computational complexity.
  arXiv  Detail & Related papers  (2025-08-23T22:27:51Z)
• Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation [45.347078403677216]
  Large-scale models (LSMs) can be an effective framework for semantic representation and understanding.<n>However, their direct deployment is often hindered by high computational complexity and resource requirements.<n>This paper proposes a novel knowledge distillation based semantic communication framework.
  arXiv  Detail & Related papers  (2025-08-04T07:47:18Z)
• Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization [49.362750475706235]
  Reinforcement Learning (RL) plays a crucial role in aligning large language models with human preferences and improving their ability to perform complex tasks.<n>We introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model.<n> Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.
  arXiv  Detail & Related papers  (2024-10-11T23:29:20Z)
• Quantum Machine Learning Architecture Search via Deep Reinforcement Learning [8.546707309430593]
  We introduce deep reinforcement learning to explore proficient QML model architectures tailored for supervised learning tasks. Our methodology involves training an RL agent to devise policies that facilitate the discovery of QML models without predetermined ansatz. Our proposed method successfully identifies VQC architectures capable of achieving high classification accuracy while minimizing gate depth.
  arXiv  Detail & Related papers  (2024-07-29T16:20:51Z)
• Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning [3.6881738506505988]
  We propose differentiable quantum architecture search (DiffQAS) to enable trainable circuit parameters and structure weights. We show that our proposed DiffQAS-QRL approach achieves performance comparable to manually-crafted circuit architectures.
  arXiv  Detail & Related papers  (2024-07-25T17:11:00Z)
• Challenges for Reinforcement Learning in Quantum Circuit Design [8.894627352356302]
  Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. We propose qcd-gym, a concrete framework formalized as a Markov decision process, to enable learning policies capable of controlling a universal set of continuously parameterized quantum gates.
  arXiv  Detail & Related papers  (2023-12-18T16:41:30Z)
• Decoding surface codes with deep reinforcement learning and probabilistic policy reuse [0.5999777817331317]
  Current quantum hardware, also known as noisy intermediate-scale quantum computers (NISQ), are still unable to carry out computations faithfully. Recent developments of machine learning (ML)-based techniques especially the reinforcement learning (RL) methods have been applied to the decoding problem. We propose a continual reinforcement learning method to address these decoding challenges.
  arXiv  Detail & Related papers  (2022-12-22T17:24:32Z)
• Quantum Architecture Search via Continual Reinforcement Learning [0.0]
  This paper proposes a machine learning-based method to construct quantum circuit architectures. We present the Probabilistic Policy Reuse with deep Q-learning (PPR-DQL) framework to tackle this circuit design challenge.
  arXiv  Detail & Related papers  (2021-12-10T19:07:56Z)
• Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork [73.94896986868146]
  Phase retrieval is an important component in modern computational imaging systems. Recent advances in deep learning have opened up a new possibility for robust and fast PR. We develop a novel framework for deep unfolding to overcome the existing limitations.
  arXiv  Detail & Related papers  (2021-01-12T08:36:23Z)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)
• SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning [102.78958681141577]
  We present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy deep reinforcement learning algorithms. SUNRISE integrates two key ingredients: (a) ensemble-based weighted Bellman backups, which re-weight target Q-values based on uncertainty estimates from a Q-ensemble, and (b) an inference method that selects actions using the highest upper-confidence bounds for efficient exploration.
  arXiv  Detail & Related papers  (2020-07-09T17:08:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.