Two-photon state reconstruction with an adaptive tomographic framework

Quantum state tomography is a mathematical process to fully determine the elements of a density matrix of a quantum state with a complete basis-measurement. The requirement of these measurements multiplies exponentially with qubits, subsequently the data post-process for large qubit states becomes a big challenge to the memory of the computer. An efficient method is thus required to precisely determine the unknown states with fewer measurements. We propose an adaptive tomographic technique with which the measurement guides itself to converge to its own target state. This adaptive tomography directly tests the hypotheses by measuring the real-time correlations within an iterative algorithm, i.e., simulated annealing method (SAM). The simulation of tomography on two-photon state justifies the validity and robustness of this method. It outperforms traditional full tomography owning owing to its higher efficiency and higher accuracy.