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Quantum coherent feedback based on waveguide quantum electrodynamics plays important roles in quantum devices design and quantum information processing. In this paper, we study the coherent dynamics of the quantum network composed of two separated resonators coupling to a waveguide. Using the dynamical feedback equation in Markovian regime, we obtain the analytical solutions to the output states for the two photons incident from the same or different directions of the waveguide. The two- photon scattering properties are analyzed based on the analytical expressions. It is found that the output states of the two photons can be controlled well by the phase induced by the single photon propagating between the two separated resonators. The results given in this paper are helpful in designing quantum devices at few-photon level. [ABSTRACT FROM AUTHOR]

An off-policy model-free reinforcement learning (RL) algorithm is proposed for a robust hierarchical game while considering incomplete information and input constraints. The robust hierarchical game exhibits characteristics of a Stackelberg–Nash (SN) game, where equilibrium points are designated as Stackelberg–Nash–Saddle equilibrium (SNE) points. An off-policy method is employed for the RL algorithm, addressing input constraints by using excitation input instead of real-time update polices as control inputs. Moreover, a model-free method is implemented for the off-policy RL algorithm, accounting for the challenge posed by incomplete information. The goal of this paper is to develop an off-policy model-free RL algorithm to obtain approximate SNE polices of the robust hierarchical game with incomplete information and input constraints. Furthermore, the convergence and effectiveness of the off-policy model-free RL algorithm are guaranteed by proving the equivalence of Bellman equation between nominal SNE policies and approximate SNE policies. Finally, a simulation is provided to verify the advantage of the developed algorithm. [ABSTRACT FROM AUTHOR]

The sparsity characteristic, which is the foundation of the compressive sensing (CS) theory, has been widely used in the inverse synthetic aperture radar (ISAR) imaging field. However, the performance of the traditional CS-based sparse aperture ISAR imaging method is limited in low signal to noise ratio (SNR) and/or sampling rate (SPR). Inspired by the low-rank property of the final ISAR image, a new sparse aperture ISAR imaging model by jointly using the sparse characteristic and low-rank property is proposed in this paper. Considering the effective solution of the proposed reconstruction model, the optimization problem is decomposed into several sub-problems under the framework of linearized alternating direction method with adaptive penalty (LADMAP) approach. Furthermore, in order to improve the computing efficiency of the proposed algorithm, a fast singular value decomposition (SVD) free algorithm is finally proposed. Both simulated and measured data experiments verify the effectiveness of the proposed algorithms, especially under the condition of low SNR and/or SPR. [ABSTRACT FROM AUTHOR]