Optimización de la Transmisión de Estados Cuánticos en Cadenas de Qubits usando Deep Reinforcement Learning y Algoritmos Genéticos

2025/10/06 by Sofía Perón Santana, Ariel Fiuri, Santana, Sofía Perón +5
Computer Science · #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Quantum-Dot Cellular Automata

paper · pdf · doi:10.48550/arxiv.2510.05010

openalex publication_date 2025/10/06 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28

Abstract

Quantum state transfer (QST) via homogeneous spin chains plays a crucial role in building scalable quantum hardware. A basic quantum state transmission protocol prepares a state in one qubit and transfers it to another through a channel, seeking to minimize the time and avoid information loss. The fidelity of the process is measured by functions proportional to the transition probability between both states. We approach this optimization problem using constant magnetic pulses and two complementary strategies: deep reinforcement learning, where an agent learns pulse sequences through rewards, and genetic algorithms, which develop candidate solutions through selection and mutation. We analyze the efficiency of both methods and their ability to incorporate physical constraints.

Citations

• Quantum state transfer performance of Heisenberg spin chains with site-dependent interactions designed using a generic genetic algorithm
• PyGAD: An Intuitive Genetic Algorithm Python Library

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