Neural network quantum state tomography in a two-qubit experiment

Understand

We study the performance of efficient quantum state tomography methods based on neural network quantum states using measured data from a two-photon experiment.

• Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators.
• While the power of these methods has been demonstrated on synthetic data, applications to real experimental data remain scarce.
• We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states.