Neural-network quantum state tomography

The reconstruction of an unknown quantum state from simple experimental measurements, quantum state tomography (QST), is a fundamental tool to investigate complex quantum systems, validate quantum devices and fully exploit quantum resources. In this talk, we introduce a novel scheme for QST using machine-learning. The wavefunction of an arbitrary many-body system is parametrized with a standard neural network, which is trained on raw data to approximate both the amplitudes and the phases of the target quantum state. This approach allows one to reconstruct highly-entangled states and reproduce challenging quantities, such as entanglement entropy, from simple measurements already available in the experiments. We show the main features of the “Neural-Network QST” and demonstrate its performances on a variety of examples, ranging from the prototypical W state, to unitary dynamics and ground states of many-body Hamiltonians in one and two dimensions.