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Topological Quantum Control of Attosecond Molecular Dynamics: A Proof-of-Principle Framework on Reduced-Dimensional Models

m el-rakhawi

Published Sep 23, 2026• 0 citations
Publisher

Abstract

Topological Quantum Control of Attosecond Molecular Dynamics: A Proof-of-Principle Framework on Reduced-Dimensional Models
DOI
10.5281/zenodo.22907945

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Topological Quantum Control of Attosecond Molecular Dynamics: A Proof-of-Principle Framework on Reduced-Dimensional Models

mohamed kamal arafa el-rakhawi

The El-Rakhawi Framework for Topological Quantum Control (DOI: 10.5281/zenodo.22907946) presents a proof-of-principle methodology integrating Uhlmann-based robustness metrics with Reinforcement Learning (PPO) to control open quantum systems governed by the Lindblad master equation. By embedding an Uhlmann Robustness Metric (URM) into the optimization reward function, the framework guides molecular dynamics along trajectories resilient to severe environmental decoherence. Simulations on a 3-level Lambda system demonstrate a target state yield improvement from ~62% (standard OCT) to ~87%. An ablation study confirms this gain is directly attributable to the URM constraint. The work maintains strict scientific honesty, acknowledging this is a reduced-dimensionality model with noted computational considerations for larger systems. Protected under sovereign intellectual property with explicit AI training restrictions and mandatory academic attribution. All rights exclusively reserved to Dr. Mohamed Kamal Arafa El-Rakhawi, establishing a reproducible blueprint for decoherence-resistant quantum control.

•Oct 15, 2026•0 citations
This paper introduces Topological Quantum Control (TQC), a framework integrating Uhlmann fidelity-based robustness metrics with AI-driven pulse optimization for attosecond molecular dynamics. Using the Lindblad master equation to model decoherence, we employ Proximal Policy Optimization (PPO) to navigate non-convex control landscapes. Validated on a three-level Lambda system under extreme noise stress-tests, TQC improves target yield from 62% to 87% compared to standard optimal control. The approach demonstrates enhanced resilience against environmental decoherence, offering a reproducible, open-source proof-of-principle for robust quantum control in complex, open systems.
ABSTRACT Femtochemistry enabled observation of atomic motion, yet controlling chemical reactions with high selectivity remains challenging due to environmental decoherence and the probabilistic nature of quantum dynamics. This paper proposes Topological Quantum Control (TQC), a theoretical framework that integrates topological invariants of open quantum systems with AI-driven pulse optimization. We formulate the control problem using the Lindblad master equation and introduce a Topological Robustness Index (TRI) based on the Uhlmann fidelity to guide optimization toward trajectories that are resilient to decoherence. We derive the adjoint-state gradient for the topological objective functional and implement a reinforcement learning agent for pulse shaping. As a proof of principle, we validate the framework on a three-level Lambda-type model system. Simulations suggest that the TQC framework can improve target product yield from approximately 62 percent under standard optimal control to approximately 87 percent under the proposed topological constraint, while maintaining high TRI values throughout the evolution. These results are obtained on a reduced-dimensionality model and serve as a conceptual demonstration; extension to realistic molecular systems remains a subject of future work. Keywords: Attosecond Physics, Topological Quantum Control, Lindblad Master Equation, Uhlmann Phase, Quantum Optimal Control, Reinforcement Learning.
The El-Rakhawi Framework for Topological Quantum Control (DOI: 10.5281/zenodo.22907946) presents a proof-of-principle methodology integrating Uhlmann-based robustness metrics with Reinforcement Learning (PPO) to control open quantum systems governed by the Lindblad master equation. By embedding an Uhlmann Robustness Metric (URM) into the optimization reward function, the framework guides molecular dynamics along trajectories resilient to severe environmental decoherence. Simulations on a 3-level Lambda system demonstrate a target state yield improvement from ~62% (standard OCT) to ~87%. An ablation study confirms this gain is directly attributable to the URM constraint. The work maintains strict scientific honesty, acknowledging this is a reduced-dimensionality model with noted computational considerations for larger systems. Protected under sovereign intellectual property with explicit AI training restrictions and mandatory academic attribution. All rights exclusively reserved to Dr. Mohamed Kamal Arafa El-Rakhawi, establishing a reproducible blueprint for decoherence-resistant quantum control.