Nicholas Metropolis Award for Outstanding Doctoral Thesis Work in Computational Physics (2020)

Machine learning offers a set of flexible and powerful algorithms to enhance the capabilities of quantum simulation platforms. Artificial neural networks trained on measurement data can be integrated in the experimental stack for a variety of tasks, such as error mitigation, detecting quantum phase transitions and improving the measurement precision. I will review a data-driven framework for reconstructing quantum states prepared by experimental quantum hardware. Once trained, the neural networks can be used to deliver precise measurements of specialized observables that are either costly or not accessible in the original experimental setup. I will present results for a cold Rydberg-atom quantum simulator and quantum chemistry calculations on a superconducting quantum hardware.