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Bulletin of the American Physical Society

New Developments in Quantum Control: Phase Space Learning Algorithms and Uncontrollable Quantum Systems

David J. Tannor

Published Mar 11, 2008• 0 citations
OpenAlex

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IEEE Access•Jan 1, 2025•1 citation
Developing efficient algorithms is an important task in the learning control of quantum systems, since most learning control problems for quantum systems involve a heavy requirement for computational resources. In this paper, we employ a learning control algorithm with an adaptive target state developed in the chemical physics community for several classes of quantum control problems. For these problems applied to some new quantum control tasks, we further demonstrate that the algorithm using an adaptive target state can be more efficient than traditional learning control algorithms using a fixed target state. In the algorithm, the target state is updated according to the renormalized fragmentary yield in the desired region (or subspace) throughout the learning iterations. The adaptive target scheme is applied to three significant quantum control tasks, including the slow collision of a sodium cation and an iodine anion, the orientation of a LiH molecule, and population transfer between subspaces. Shaped laser pulses are obtained using the learning control algorithm, and numerical results are presented to demonstrate the advantages of the adaptive target scheme over the algorithm using a fixed target state. The adaptive target scheme is especially useful for learning control problems of quantum systems where the target state is not unique or known.
This paper summarizes several recent achievements in the area of learning control of quantum systems and draw several new directions for future research. Three learning algorithms including gradient method, differential evolution and reinforcement learning are introduced for quantum control. Quantum state control in closed and open quantum systems is analyzed, where gradient method and differential evolution are employed, respectively. The approach of deep reinforcement learning for quantum gate control is introduced, and a sampling-based learning control is illustrated for robust control of quantum gates.
This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. This research aims to use the complementary strengths of DQN and PPO algorithms to develop robust and adaptive control policies for noisy and uncertain quantum systems. We comprehensively analyse the proposed ensemble learning, including algorithmic details, implementation specifics, and experimental results. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the ensemble approach in learning control strategies for manipulating quantum systems towards a random target state. The results highlight the potential of ensemble RL techniques in addressing the challenges of quantum control tasks, such as system noise and dynamics. By integrating multiple RL agents within an ensemble framework, We aim to advance current developments in quantum control and create a new path for the development of adaptive control systems for quantum systems. The performance of the ensemble model is assessed against Gradient Ascent Pulse Engineering (GRAPE) and robust Model Predictive Control (MPC) to demonstrate its efficiency in highly challenging and noisy environments.