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Quantum key distribution (QKD) allows two distant parties to share encryptionkeys with security based on laws of quantum mechanics. In order to share thekeys, the quantum bits have to be transmitted from the sender to the receiverover a noisy quantum channel. In order to transmit this information, efficientencoders and decoders need to be designed. However, large-scale design ofquantum encoders and decoders have to depend on the channel characteristics andrequire look-up tables which require memory that is exponential in the numberof qubits. In order to alleviate that, this paper aims to design the quantumencoders and decoders for expander codes by adapting techniques from machinelearning including reinforcement learning and neural networks to the quantumdomain. The proposed quantum decoder trains a neural network which is trainedusing the maximum aposteriori error for the syndromes, eliminating the use oflarge lookup tables. The quantum encoder uses deep Q-learning based techniquesto optimize the generator matrices in the quantum Calderbank-Shor-Steane (CSS)codes. The evaluation results demonstrate improved performance of the proposedquantum encoder and decoder designs as compared to the quantum expander codes.

Encoders and Decoders for Quantum Expander Codes Using Machine Learning