Dynamic Topological Adaptive Quantum Entanglement Network

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to constructing quantum entanglement networks that dynamically adapt to environmental fluctuations. The core claim is that real-time monitoring of decoherence rates and environmental noise, coupled with adaptive network topology adjustments, can maximize the efficiency of entanglement information transmission. The proposed mechanism utilizes machine learning algorithms to analyze quantum bit (qubit) quantum state information, predict decoherence rates, and automatically optimize qubit-to-qubit connections, resulting in a self-adaptive entanglement network. Existing quantum entanglement networks largely rely on fixed topologies, lacking the adaptability necessary to contend with dynamic environmental changes. This research bridges the gap by integrating machine learning with quantum entanglement networks, enabling a truly adaptive entanglement network. The key performance indicator (KPI) for this network is the entanglement fidelity (EF), defined as: EF = ∑i |⟨ψ⟩i|2 where ψi represents the entangled state of qubits i and the summation is performed over all qubits in the network. The network's overall efficiency is directly correlated with maintaining high entanglement fidelity over extended periods. The goal is to minimize the decoherence rate (R) which is defined as: R = d(ρ)/dt where ρ is the density matrix of the quantum system. The adaptive network topology dynamically adjusts to minimize R, thus maximizing the entanglement fidelity. Further, the network's performance is evaluated using the average entanglement distribution (AED) calculated as: AED = (1/N) ∑j=1N Ej where N is the number of qubits and Ej is the entanglement between qubit j and the rest of the network.

DOI: 10.5281/zenodo.22188858

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