Simultaneous Discovery of Quantum Error Correction Codes and Encoders with a Noise-Aware Reinforcement Learning Agent
arXiv:2311.04750 · doi:10.1038/s41534-024-00920-y
Abstract
In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, allowing us to scale our results to 20 physical qubits and distance 5 codes. Moreover, we introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.
14 p.main + 9 p.appendix. Codes available on github
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Cited by in corpus (5)
- Artificial Intelligence for Quantum Computing
- Enhanced Qubit Readout via Reinforcement Learning
- Quantum noise modeling through Reinforcement Learning
- Artificial intelligence for representing and characterizing quantum systems
- Automated discovery and optimization of autonomous quantum error correction codes for a general open quantum system