Fig. 1: QEC code and encoding discovery using a noise-aware RL meta-agent. | npj Quantum Information

Fig. 1: QEC code and encoding discovery using a noise-aware RL meta-agent.

From: Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent

Fig. 1

A set of error operators, a gate set, and qubit connectivity are chosen. Different error models can be considered by varying some noise parameters, which are fed as an observation to the agent. The agent then builds a circuit using the available gate set and connectivity that detects the most likely errors from the target error model by using a reward based on the Knill-Laflamme QEC conditions according to Eq. (2). After training, a single RL agent is able to find suitable encodings for different noise models, which are able to encode any state \(\left\vert \psi \right\rangle\) of choice.

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