Applications of machine learning and related techniques to quantum control problems

NISQ quantum computers require precise quantum control to achieve the necessary high fidelity operations. Machine learning offers tools that can be applied to this task. For example, reinforcement learning is a promising paradigm for universal quantum control. In the case of superconducting qubits, this requires explicit bounds on qubit leakage. To achieve high fidelity operations, quantum control parameters are fine tuned experimentally. Machine learning techniques can be applied in the optimization process. Cross entropy benchmarking is a technique to extract the experimental fidelity for generic operations with high precision, providing the cost function for the optimization loop.