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