Active steering into quantum stabilizer codespace with reinforcement learning

The quantum error correction protocol has been a practical problem in quantum computation, especially in measuring high-weight stabilizers and decoding the error syndrome to find recovery operators. We propose a technique to actively maintain a quantum stabilizer codestate in the codespace even under the influence of decoherence. Our protocol uses continuous measurements of operators from the stabilizer algebra to perform Hamiltonian corrections. The measurement operators and the correction strengths are provided by a reinforcement learning agent. We process the measurement data by first applying an exponential averaging filter and then stacking the previous measurement outcomes before sending them to a reinforcement learning agent. The agent then provides correction strengths and the subsequent measurement operators. We demonstrate that this protocol can evolve any unknown quantum state into a stabilizer code state, and also maintain it within the codespace. This technique is particularly useful since it is scalable to higher dimensional quantum stabilizer codes.