Title:An error-mitigated photonic quantum circuit Born machine

Abstract: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 can be implemented on shallow circuits. Within the framework of photonic quantum computing, we present a QCBM designed for linear optics. We show that a new error mitigation technique, called recycling mitigation, greatly improves the training of QCBMs in realistic scenarios with photon loss, both through simulations and an experiment on a quantum photonic integrated processor.