Reinforcement Learning for Enhanced Advanced QEC Architecture Decoding

TL;DR

Decoding for advanced quantum error correction codes in distributed architectures faces real-time latency and syndrome-dependent error patterns. The authors introduce SPA-MARL, a synergy-aware MARL framework with a learnable coupling score $\lambda(s) \in [0,1]$ that dynamically selects between independent and coordinated decoding, and a Q-function decomposition $Q_{tot}$ that blends local and cross-agent information. Key contributions include a +7.4% improvement over QMIX in Stage 1, interpretable decomposition patterns with strong synergy–complexity correlation, and an end-to-end mapping from synergy to flying-qubit channel intensity with hardware feedback for meta-learning. This approach enables robust, scalable, and hardware-aware distributed QEC in modular quantum systems, supporting practical deployment and co-design of software and hardware for fault-tolerant quantum computing.

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.

Figures (5)

• Figure 1: Logical-to-Physical Mapping in the Synergy-Aware Distributed Decoder
• Figure 2: Two-layer BB code architecture illustrating quantum switch–mediated connections and planar projections.
• Figure 3: Stage 1: Algorithm validation showing SPA-MARL improvement over QMIX baseline.
• Figure 4: Hardware adaptation validation demonstrating robust performance across diverse quantum computing platforms.
• Figure 5: Total Latency Comparison: Single-QPU vs Two-QPU Distributed Across Different Code Distances