Quantum process tomography with neural networks.

Neural networks are universal function approximators that can be trained as parametric models to solve various types of problems. In recent times, neural networks have shown significant successes in representing many-body quantum states and learning these representations from data. In this work, we apply neural networks to the task of quantum process tomography (QPT) for both discrete- (qubits) and continuous-variable (bosonic) quantum systems. We show that neural networks can be used to successfully reconstruct quantum processes using Kraus operators or Chi-matrix representations. Our approach reconstructs quantum process representations for systems with up to 5 qubits. We benchmark our neural-network approach against state-of-the-art reconstruction algorithms to demonstrate fast convergence, robustness to noisy data, and the ability to work with a reduced amount of data. We also show results of QPT on real experimental data from both qubit and bosonic systems.