This chapter focuses on the construction of a high-fidelity entangled quantum teleportation channel framework, addressing the critical issues of “entanglement death” and quantum decoherence in quantum teleportation. The framework is built upon a quantum entanglement evolution model that characterizes the entanglement dynamics in both locally independent and locally common noise environments. The model analyzes the entanglement evolution of two independent atomic systems and two three-level atomic systems, providing insights into the conditions under which entanglement death occurs. Additionally, the chapter explores quantum decoherence in the Tavis-Cummings (T-C) and Jaynes-Cummings (J-C) models, laying the groundwork for the development of immune noise models. Two key immune noise models are proposed: one based on density matrix analysis and another utilizing decoherence-free subspaces (DFS) to counteract collective noise. Finally, the chapter introduces a novel quantum graph state coding method for tree and forest graphs, enabling efficient channel capacity coding under noise. This approach significantly improves computational speed and noise tolerance, providing a robust framework for high-fidelity quantum teleportation.

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Construction of a Framework for High-Fidelity Entangled Quantum Teleportation Channel

  • Dongfen Li

摘要

This chapter focuses on the construction of a high-fidelity entangled quantum teleportation channel framework, addressing the critical issues of “entanglement death” and quantum decoherence in quantum teleportation. The framework is built upon a quantum entanglement evolution model that characterizes the entanglement dynamics in both locally independent and locally common noise environments. The model analyzes the entanglement evolution of two independent atomic systems and two three-level atomic systems, providing insights into the conditions under which entanglement death occurs. Additionally, the chapter explores quantum decoherence in the Tavis-Cummings (T-C) and Jaynes-Cummings (J-C) models, laying the groundwork for the development of immune noise models. Two key immune noise models are proposed: one based on density matrix analysis and another utilizing decoherence-free subspaces (DFS) to counteract collective noise. Finally, the chapter introduces a novel quantum graph state coding method for tree and forest graphs, enabling efficient channel capacity coding under noise. This approach significantly improves computational speed and noise tolerance, providing a robust framework for high-fidelity quantum teleportation.