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Finding optimal ways to protect quantum states from noise remains anoutstanding challenge across all quantum technologies, and quantum errorcorrection (QEC) is the most promising strategy to address this issue.Constructing QEC codes is a complex task that has historically been powered byhuman creativity with the discovery of a large zoo of families of codes.However, in the context of real-world scenarios there are two challenges: thesecodes have typically been categorized only for their performance under anidealized noise model and the implementation-specific optimal encoding circuitis not known. In this work, we train a Deep Reinforcement Learning agent thatautomatically discovers both QEC codes and their encoding circuits for a givengate set, qubit connectivity, and error model. We introduce the concept of anoise-aware meta-agent, which learns to produce encoding strategiessimultaneously for a range of noise models, thus leveraging transfer ofinsights between different situations. Moreover, thanks to the use of thestabilizer formalism and a vectorized Clifford simulator, our RL implementationis extremely efficient, allowing us to produce many codes and their encodersfrom scratch within seconds, with code distances varying from 3 to 5 and withup to 20 physical qubits. Our approach opens the door towards hardware-adaptedaccelerated discovery of QEC approaches across the full spectrum of quantumhardware platforms of interest.

Simultaneous Discovery of Quantum Error Correction Codes and Encoders with a Noise-Aware Reinforcement Learning Agent