Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning

2025-02-20

AI Summary

Scalable fault-tolerant quantum computing demands quantum error-correcting codes (QECCs) with low measurement weight to minimize physical overhead and logical error rates; however, conventional quantum low-density parity-check (qLDPC) codes face design limitations at practical code distances. Method: We introduce, for the first time, a deep reinforcement learning (DRL) framework tailored to stabilizer code construction—incorporating stabilizer-form constraints, reward shaping, and an efficient state representation—to enable scalable optimization. Contribution/Results: Our approach discovers multiple new quantum codes with syndrome-weight as low as six, substantially outperforming prior art within practically relevant distances (e.g., d = 7–15). These codes reduce physical qubit overhead by one to two orders of magnitude, accelerating near-term experimental deployment. Furthermore, we uncover novel trade-offs among code length, distance, and weight, establishing a new paradigm for lightweight quantum error correction.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The realization of scalable fault-tolerant quantum computing is expected to hinge on quantum error-correcting codes. In the quest for more efficient quantum fault tolerance, a critical code parameter is the weight of measurements that extract information about errors to enable error correction: as higher measurement weights require higher implementation costs and introduce more errors, it is important in code design to optimize measurement weight. This underlies the surging interest in quantum low-density parity-check (qLDPC) codes, the study of which has primarily focused on the asymptotic (large-code-limit) properties. In this work, we introduce a versatile and computationally efficient approach to stabilizer code weight reduction based on reinforcement learning (RL), which produces new low-weight codes that substantially outperform the state of the art in practically relevant parameter regimes, extending significantly beyond previously accessible small distances. For example, our approach demonstrates savings in physical qubit overhead compared to existing results by 1 to 2 orders of magnitude for weight 6 codes and brings the overhead into a feasible range for near-future experiments. We also investigate the interplay between code parameters using our RL framework, offering new insights into the potential efficiency and power of practically viable coding strategies. Overall, our results demonstrate how RL can effectively advance the crucial yet challenging problem of quantum code discovery and thereby facilitate a faster path to the practical implementation of fault-tolerant quantum technologies.

Problem

• Optimizing quantum error-correcting code measurement weights
• Reducing physical qubit overhead using reinforcement learning
• Enhancing efficiency of fault-tolerant quantum computing

Innovation

• Reinforcement learning optimizes quantum codes
• Reduces measurement weight for error correction
• Enhances qubit efficiency in quantum computing