In this paper, an optimal asymmetric controlled teleportation protocol is proposed, where a three-dimensional (3D) GHZ entangled state is utilized to teleport an arbitrary unknown two-dimensional (2D) qubit and the correlation between the two entangled qutrits caused by their continuous transmission through a noisy channel is considered. We design a high-dimensional feed-forward control operator and use weak measurement reversal instead of the unitary operations in standard teleportation to mitigate the reduction in fidelity caused by noise. We further derive the average fidelity and overall success probability of the proposed teleportation protocol, and simulate its performance under correlated amplitude damping (CAD) and amplitude damping (AD) noise channels. The simulation results show that our protocol significantly improves the average fidelity under CAD noise. In particular, under AD noise, the average fidelity remains constant at 1. Moreover, we derive the optimal overall success probability without compromising fidelity. To verify the superiority of the feed-forward control method, we also calculate and analyze the performance of an asymmetric controlled teleportation protocol using only environment-assisted measurement (EAM), and compare it with the performance of our protocol through simulation. The results demonstrate that our protocol outperforms the protocol using only EAM methods in both CAD and AD noise.

Due to its property of not requiring prior knowledge of the environment, reinforcement learning (RL) has significant potential for solving quantum control problems. In this work, we investigate the effectiveness of continuous control policies based on deep deterministic policy gradient. To achieve good control of quantum systems with high fidelity, we propose an auxiliary task-based deep RL (AT-DRL) for quantum control. In particular, we design an auxiliary task to predict the fidelity value, sharing partial parameters with the main network (from the main RL task). The auxiliary task learns synchronously with the main task, allowing one to extract intrinsic features of the environment, thus aiding the agent to achieve the desired state with high fidelity. To further enhance the control performance, we also design a guided reward function based on the fidelity of quantum states that enables gradual fidelity improvement. Numerical simulations demonstrate that the proposed AT-DRL can provide a good solution to the exploration of quantum dynamics. It not only achieves high task fidelities but also demonstrates fast learning rates. Moreover, AT-DRL has great potential in designing control pulses that achieve effective quantum state preparation.

Time-varying noises are one of the reasons that make it difficult for quantum systems to complete control tasks. How to quantify the influence of time-varying noises on control results and how to design a control law that can resist time-varying noises are two important problems. In this paper, the adversarial learning is introduced into quantum control and the loss function under the worst-case noise is used as a way to quantify the impact of time-varying noises on control performance. We utilize the Gradient Ascent Pulse Engineering (GRAPE) technique to search the worst-case noise and meanwhile offer a strategy to improve the robustness of the control law. Simulation experiments on a two-qubit system and a four-qubit system show that the found noises indeed can act as worst-case noises. Furthermore, the optimized control laws demonstrate good robustness to time-varying noises in state preparation tasks.

Reinforcement learning has been intensively applied to tackle complex quantum control problems owing to its adaptability in dynamic environments. However, commonly used reinforcement learning algorithms are restricted to selecting actions from a discrete action space, which may result in inaccurate control. In order to surpass this constraint, we propose an improved continuous reinforcement learning algorithm that can generate control policies in a continuous action space, enabling precise control of quantum systems. Moreover, a guided reward function design method is proposed to guide the learning process toward higher fidelity. Numerical results demonstrate that the proposed continuous reinforcement learning algorithm with a guided reward function can capably prepare states on one-qubit and two-qubit systems.

For stochastic quantum systems with measurement feedback, this article proposes a rapid switching control scheme based on state space partition and realizes the rapid stabilization of an eigenstate of an observable operator. Meanwhile, we apply the proposed scheme to the preparation of typical entangled states in multiqubit systems. In view of the convergence obstacle caused by the symmetric structure of the state space, especially in the case with degenerate observable operators, we first partition the state space into a subset containing the target state and its complement to distinguish the target state from its antipodal points, and then design the corresponding control laws in these two subsets, respectively, by using different Lyapunov functions. The interaction Hamiltonians are also constructed to drive the system state to the desired subset first, and further to the target state. In particular, the control law designed in the undesired subset guarantees the strictly monotonic descent of the corresponding Lyapunov function, which makes the system trajectory switch between the two subsets at most twice and has the potential to speed up the convergence process. We also prove the stability of the closed-loop system with the proposed switching control law based on the stochastic Lyapunov stability theory. By applying the proposed switching control scheme to a three-qubit system, we achieve the preparation of a GHZ state and a W state.

The bounded stability problem of linear mixed quantum-classical feedback control systems with time delays is investigated in this paper. The whole feedback system is composed of a quantum plant, a classical controller, and some interconnection devices. Two classes of feedback delays in the feedback loop are considered: constant delays and time-varying delays. The stability of the closed-loop systems under these two classes of time delays is analyzed by constructing different Lyapunov-Krasovskii functions and the corresponding stability criteria are derived, respectively. In particular, a weighting matrix is introduced into stability analysis and the obtained stability results are less conservative than relevant results for this class of quantum systems.

For two-qubit systems with a degenerate measurement operator, this paper presents a control approach to achieve rapid stabilization to a target Bell state. This approach uses two control channels where the control laws associated with these two channels are designed as a constant and a switching control law, respectively. In the control scheme, the system state space is divided into two parts: the set containing the target state, and its complementary set. We design corresponding control laws in these two sets via different Lyapunov functions. In this scheme, the system trajectory switches only one or two times between the two sets and the convergence rate to the target state can be improved. We provide the conditions on the control Hamiltonians to stabilize the target state by analyzing the stability of the closed-loop system.

The Greenberger-Horne-Zeilinger (GHZ) entangled states are a typical class of entangled states in multiparticle systems and play an important role in the applications of quantum communication and quantum computation. For a general quantum system of qubits, degenerate measurement operators are often met, which cause the convergence obstacle in the state preparation or stabilization problem. This paper first generalizes the traditional quantum state continuous reduction theory to the case of a degenerate measurement operator and chooses a measurement operator for an arbitrarily given target GHZ entangled state, then presents a state stabilization control strategy based on the Lyapunov method and achieves the feedback preparation of the target GHZ state. In our stabilization strategy, we separate the target GHZ state and all the other GHZ states that often form the equilibrium points of the closed-loop system by dividing the state space into several different regions; and formally design a switching control law between the regions, which contains the control Hamiltonians to be constructed. By analyzing the stability of the closed-loop system in the different regions, we propose a systematic method for constructing the control Hamiltonians and solve the convergence problem caused by the degenerate measurement operator. The global stability of the whole closed-loop stochastic system is strictly proved. Also, we perform some simulation experiments on a three-qubit system and prepare a three-qubit GHZ entangled state. At the same time, the simulation results show the effectiveness of the switching control law and the construction method for the control Hamiltonians proposed in this paper.