Quantum-classical hybrid learning system using optimal transport loss and parameter shift rules

Abstract

A quantum computation unit, a classical computation unit, and a management unit are provided to learn distribution of a quantum dataset. A structure of a generator is determined from multiple samples of latent variables and values of quantum circuit parameters. A ground cost and a gradient of the ground cost are calculated from the generator and the quantum dataset. An optimal transport loss and a gradient of the optimal transport loss are calculated using the ground cost and the gradient of the ground cost. An updating process is executed that updates the quantum circuit parameter using the gradient of the optimum transport loss, thereby reducing the optimum transport loss. The updating process is repeated until the optimum transport loss converges.