A Decoherence Minimization Theorem using the Lindblad Master Equation with Reinforcement Learning–based Quantum Control

Abstract

Quantum decoherence resulting from environmental interaction is still a key issue that prevents viable quantum computing systems. However, existing classical quantum control techniques in most cases depend on deterministic optimization approaches and often show limited flexibility under changing, noisy conditions. In order to overcome this limitation, this paper introduces the reinforcement learning (RL)–based quantum control framework that integrates the Lindblad master equation for flexible decoherence minimization in open quantum systems. The proposed framework utilizes the Lindblad formalism to describe quantum state evolution, and relies on a Deep Q-Network (DQN) model that learns optimal quantum state fidelity control policies to prevent environmental decoherence by interacting consistently with the quantum environment. The framework rests on a decoherence minimization theorem that captures that the learned policy for control converges to enhanced quantum coherence. The proposed RL–Lindblad framework is evaluated under varying noisy quantum environments, demonstrating a quantum state fidelity of 0.973 and a decoherence reduction of 91.8%, outperforming classical quantum control and reinforcement learning techniques. These findings show the efficacy, robustness and scalability of the proposed framework used in adaptive quantum control and help guide intelligent quantum computing systems towards realistic noisy environments.