Deep reinforcement learning (DRL) has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel DRL approach by constructing a curriculum consisting of a set of intermediate tasks defined by fidelity thresholds, where the tasks among a curriculum can be statically determined before the learning process or dynamically generated during the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based DRL (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical comparison with the traditional methods [gradient method (GD), genetic algorithm (GA), and several other DRL methods] demonstrates that CDRL exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with few control pulses.
Deep reinforcement learning (DRL) has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel DRL approach by constructing a curriculum consisting of a set of intermediate tasks defined by fidelity thresholds, where the tasks among a curriculum can be statically determined before the learning process or dynamically generated during the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based DRL (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical comparison with the traditional methods [gradient method (GD), genetic algorithm (GA), and several other DRL methods] demonstrates that CDRL exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with few control pulses.
Deep reinforcement learning (DRL) has been recognized as a powerful tool in quantum physics, where DRL's reward design is nontrivial but crucial for quantum control tasks. To address the problem of over-reliance on human empirical knowledge to design DRL's rewards, we propose a DRL with a novel reward paradigm designed by the learning process information (DRL-LPI), where the learning process information (LPI) comprises the state information and the experiences. In DRL-LPI, the state information after being classified by a fidelity threshold, and the experiences are first stored simultaneously in the respective sequences, and this process is repeated until a similar-segment ends. Then, the stored state information is converted to the real value and used to design the reward value by applying a self-amplitude function. Next, the designed reward values are integrated with the stored experiences to compose transitions for DRL's training. Through comparisons to five representative reward schemes, the proposed DRL-LPI is evaluated on two typical quantum control tasks, i.e., the spin-(1/2) quantum state control and many-coupled qubits state control, and the experimental results show the superior learning efficiency and control performance of the proposed approach. More results show that DRL-LPI exhibits the ability to learn the control strategy with few control actions compared to stochastic gradient descent (SGD) and genetic algorithm (GA).
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.
Deep reinforcement learning (DRL) has been widely used in many quantum control tasks, in which rewards are used to guide the agent in acquiring an optimal control strategy. However, designing suitable rewards is usually challenging, which seriously affects the DRL's application performance, especially for complex quantum control tasks. In this article, we propose a simple and general reward design method by constructing some events, where events are defined by fidelity intervals. In the proposed event-based DRL (EDRL), events are used to describe the evolution of a quantum system. Rewards are designed according to several defined events rather than a large number of specific evolved quantum states, thereby resulting in a reduction in dimensions to be considered. With designed event-attributed rewards, the agent can capture the main evolution characteristics of a quantum system, and then obtain an effective control strategy. The effectiveness of the proposed EDRL method is verified by three quantum systems: i.e., 1) the one-qubit closed quantum system, 2) the two-qubit closed quantum system, and 3) the two-level open quantum system. Numerical comparisons demonstrate that the proposed EDRL outperforms the traditional DRL algorithms (deep Q-network and proximal policy optimization) with some representative reward methods for solving quantum control problems.
This paper explores the application of Deep Reinforcement Learning (DRL) for optimizing quantum resource allocation in quantum computing. Traditional quantum resource allocation often relies on manual design and expert knowledge, which can be computationally expensive and limit scalability. We propose a novel framework utilizing DRL to automate this process, learning optimal strategies for allocating quantum resources (qubits, gates, and time) to maximize the performance of quantum algorithms. The core of our approach involves constructing a DRL model where the agent learns to make decisions regarding resource allocation based on the current state of the quantum system and the desired algorithm outcome. We define a state space representing the quantum system's configuration, action space encompassing various resource allocation options, and a reward function that reflects the algorithm's success. Through this framework, we aim to significantly enhance the efficiency and performance of quantum computations. This work presents a foundational approach, paving the way for more sophisticated DRL-based quantum resource allocation strategies.
We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.
This paper investigates quantum gate control problems using the deep reinforcement learning algorithm, i.e., a model-free machine learning method. We implement the twin delayed deep deterministic policy gradient (TD3) algorithm to search for piece-wise constant control pulses for quantum gates through the trail interaction with the quantum system. Simulation results on four typical gates, including three one-qubit gates and a two-qubit CNOT gate, demonstrate that deep reinforcement learning exhibits improved performance for quantum gate control tasks. By setting punishment for steps in the reward function, DRL can automatically find a shorter control sequence than the traditional gradient-based algorithm (e.g., GRAPE algorithm) and the evolutionary algorithm (e.g., DE algorithm) while maintaining high control precision.