  Generative machine learning models aim to learn the underlying distribution
of the data in order to generate new samples. Quantum circuit Born machines
(QCBMs) are a popular choice of quantum generative models, which are
particularly well suited to near-term devices since they can be implemented on
shallow circuits. Within the framework of photonic quantum computing, we design
and simulate a QCBM that can be implemented with linear optics. We show that a
newly developed error mitigation technique called recycling mitigation greatly
improves the training of QCBMs in realistic scenarios with photon loss.
