利用強化學習發現高效低權重量子糾錯碼

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

February 20, 2025

作者: Austin Yubo He, Zi-Wen Liu

cs.AI

摘要

實現可擴展的容錯量子計算預計將依賴於量子糾錯碼。在追求更高效的量子容錯技術過程中，一個關鍵的編碼參數是提取錯誤信息以實現錯誤校正的測量權重：由於更高的測量權重需要更高的實施成本並引入更多錯誤，因此在編碼設計中優化測量權重至關重要。這正是量子低密度奇偶校驗（qLDPC）碼受到廣泛關注的基礎，其研究主要集中在漸近（大碼限）特性上。在本研究中，我們引入了一種基於強化學習（RL）的多功能且計算效率高的穩定子碼權重降低方法，該方法生成了新的低權重碼，在實際相關的參數範圍內顯著超越了現有技術水平，並大幅擴展了之前可及的小距離範圍。例如，對於權重為6的編碼，我們的方法相比現有結果在物理量子比特開銷上節省了1到2個數量級，並將開銷降至適合近期實驗的可行範圍。我們還利用RL框架研究了編碼參數之間的相互作用，為實際可行的編碼策略的潛在效率和能力提供了新的見解。總體而言，我們的結果展示了RL如何有效推進量子碼發現這一關鍵而具挑戰性的問題，從而加速容錯量子技術的實際應用進程。

English

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.