Efficient Quantum Tomography with Fidelity Estimation.

We propose a machine-learning-based quantum state tomography scheme for pure states, along with a built-in fidelity estimation approach to access the reliability of the tomographic state. We prove the validity of the scheme theoretically and perform computer-simulated experiments on several representative target quantum states such as the W, cluster and dimer states. We found that the required number of measurements to meet the convergence criterion does not grow exponentially in the number of qubits, thus the scheme achieves high efficiency that is crucial for the tomography of large-scale quantum states realized in the laboratory.