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Journal of Contemporary Physics (Armenian Academy of Sciences)

Automated Optimization of Laser Fields for Quantum State Manipulation

Ruben O. Sahakyan, Romik Sargsyan, Э. Погосян, Karen Arzumanyan, Harutyun Hambardzumyan, E. A. Gazazyan

Published Dec 1, 2025• 2 citations
Publisher

Abstract

Abstract This paper proposes a numerical approach to the problem of inverse quantum control with a focus on the automated formation of external laser control fields that ensure high-precision and controlled population transfer in multilevel quantum systems. The method is based on optimizing the parameters of control pulses using gradient algorithms in combination with automatic differentiation, which allows for effective control of the physical characteristics of laser fields and increases the stability of the transfer process. The obtained results provide a foundation for the development of more general quantum control methods focused on experimental implementation on real physical platforms.

DOI
10.1134/s1068337226700131

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Abstract This paper proposes a numerical approach to the problem of inverse quantum control with a focus on the automated formation of external laser control fields that ensure high-precision and controlled population transfer in multilevel quantum systems. The method is based on optimizing the parameters of control pulses using gradient algorithms in combination with automatic differentiation, which allows for effective control of the physical characteristics of laser fields and increases the stability of the transfer process. The obtained results provide a foundation for the development of more general quantum control methods focused on experimental implementation on real physical platforms.
A gradient-based optimization approach combined with automatic differentiation is employed to ensure high accuracy and scalability when working with high-dimensional parameter spaces. Numerical simulations confirm the effectiveness of the proposed method: the population is reliably transferred to the target state with minimal occupation of intermediate levels, while the control pulses remain smooth and physically implementable. The developed framework serves as a universal and experimentally applicable tool for automated control pulse design in quantum systems. It is particularly useful in scenarios where analytical methods or manual parameter tuning--such as standard schemes like STIRAP--prove to be inefficient or inapplicable.
We investigate two classes of quantum control problems by using frequency-domain optimization algorithms in the context of ultrafast laser control of quantum systems. In the first class of problems, the system model is known and a frequency-domain gradient-based optimization algorithm is applied for searching an optimal control field to selectively and robustly manipulate the population transfer in atomic rubidium. The other class of quantum control problems involves an experimental system with an unknown model. In this case, we introduce a differential evolution algorithm with a mixed strategy to search for optimal control fields and demonstrate the capability in an ultrafast laser control experiment for the fragmentation of Pr(hfac)3molecules.
Coherent quantum control shows promise in preparing and manipulating internal states within a quantum system coherently. Specifically, it aims to smoothly transition the system from an initial state to a desired target state using external electromagnetic fields. This transfer of population between ground states is crucial in various quantum processes, including applications in photochemistry for controlling reactions with fast laser fields, enhancing Nuclear Magnetic Resonance images, quantum optics and quantum information science. Quantum information processing manipulates data with unitary operators called quantum logic gates, which control state transitions in qubits. These gates are vital for future quantum computers, requiring high efficiency and robustness despite experimental limitations and errors, like inconsistent control fields. The primary goal in quantum computing is to transfer population from the ground state to the target state with a high fidelity , good robustness and minimum interaction time between the electromagnetic fields and the system. Currently, creating a control method that meets all three criteria is a significant challenge in quantum information. However, by merging reverse engineering using the Lewis-Riesenfeld dynamic invariant with optimal control theory, we've shown that designing such a control field can be simplified into a Lagrange optimization problem with integral constraints. This hybrid method, called RIO (Robust Inverse Optimization), is adaptable and applicable to diverse quantum control challenges.
This paper explores the application of deep learning techniques to the field of quantum state optimization control. Traditional methods for optimizing control pulses for quantum systems are often computationally intensive and may struggle to handle the complexity of realistic quantum systems. This research proposes a novel framework utilizing deep learning to automatically optimize control pulses, aiming for precise control and state evolution of quantum systems. The core of the approach involves constructing a deep learning model where the quantum system state is the input and the control pulse is the output. This model is trained using either reinforcement learning or supervised learning algorithms. The potential of this method to significantly accelerate the optimization process and facilitate control of complex quantum systems is investigated. The resulting optimized control pulses demonstrate improved performance compared to conventional optimization strategies. This work lays the foundation for automated quantum control and opens new possibilities for manipulating quantum systems with greater precision and efficiency.
We discuss recent advances in optimal control theory (OCT) related to the investigation of the impact of control field noise on controllability of quantum dynamics. Two numerical methods, the gradient method and the iteration method, are paid particular attention. We analyze the problem of designing noisy control fields to maximize the vibrational transition probability in diatomic quantum systems, e.g. the HF and OH molecules. White noise is used as an additive random variable in the amplitude of the control field. It is demonstrated that the convergence is faster in the presence of noise and population transfer is increased by 0.04% for small values of noise compared to the field amplitude.
We implement a quantum optimal control algorithm based on automatic differentiation and harness the acceleration afforded by graphics processing units (GPUs). Automatic differentiation allows us to specify advanced optimization criteria and incorporate them in the optimization process with ease. We show that the use of GPUs can speedup calculations by more than an order of magnitude. Our strategy facilitates efficient numerical simulations on affordable desktop computers and exploration of a host of optimization constraints and system parameters relevant to real-life experiments. We demonstrate optimization of quantum evolution based on fine-grained evaluation of performance at each intermediate time step, thus enabling more intricate control on the evolution path, suppression of departures from the truncated model subspace, as well as minimization of the physical time needed to perform high-fidelity state preparation and unitary gates.