Objective function maximization by machine learning for gate-model quantum computers

We define a framework for objective function estimation and maximization of arbitrary computational problems in gatemodel quantum computers. The method significantly reduces the costs of the objective function estimation and provides an estimate of the new state of the quantum computer. The framework integrates an objective function extension procedure, a segmentation algorithm that utilizes the gate parameters of the quantum computer, and a machine-learning unit for the quantum state prediction. The results are particularly convenient for the performance optimization of experimental gate-model quantum computations.