LAUSR

Abstract Reconstructing high-dimensional quantum state accurately with noise is a challenging problem, because of the probabilistic measurements of quantum states and the exponential growth of the computation in terms of the number of qubits. In this paper, an improved Alternating Direction Multiplier Method (ADMM) is proposed for the quantum state estimation based on the lower limit of the measurement rate inferred by Compressive Sensing (CS) theory. The proposed algorithm solves the density matrix correlation subproblems by introducing a proximal gradient step to avoid large-scale matrix inversion. Furthermore, it reduces the computational complexity by changing the order of operations. The algorithm we proposed can dramatically reduces the reconstruction time on the premise of achieving high reconstruction accuracy. When the number of qubits n = 14 and sampling rate η = 0.0819%, it takes about 3 hours to reconstruct the density matrix of a pure quantum state with the reconstruction error of 4.97e-2 and fidelity of 98.79%.