Optimizing the transmission of quantum states in chains of qubits using Deep Reinforcement Learning and genetic algorithms

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.