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arXiv:2510.05010

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

Sofía Perón Santana, Ariel Fiuri, Omar Osenda, Martin Ariel Ariel Dominguez

Published Oct 6, 2025• 0 citations
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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.

DOI
10.48550/arxiv.2510.05010

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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.
La transferencia de estado cuántico (QST) a través de cadenas de espín homogéneas desempeña un papel crucial en la construcción de hardware cuántico escalable. Un protocolo básico de transmisión de estados cuánticos prepara un estado en un qubit y lo transfiere a otro a través de un canal, buscando minimizar el tiempo y evitar pérdida de información. La fidelidad del proceso se mide mediante funciones proporcionales a la probabilidad de transición entre ambos estados. Abordamos este problema de optimización mediante pulsos magnéticos constantes y dos estrategias complementarias: aprendizaje por refuerzo profundo, donde un agente aprende secuencias de pulsos mediante recompensas, y algoritmos genéticos, que desarrollan soluciones candidatas mediante selección y mutación. Analizamos la eficiencia de ambos métodos y su capacidad para incorporar restricciones físicas.
In recent years, control methods based on different optimization techniques have shed light on the possibilities of processing information in many quantum systems. When exploring the transmission of quantum states, faster transmission times are mandatory to avoid the deleterious effects of multiple sources of decoherence that spoil the transmission process. In particular, using Reinforcement Learning to devise sequences of step-wise external controls provides good transfer policies at short transmission times. We present two approaches to control the transmission of quantum states in qubit chains using external controls to force the dynamical evolution of the chain state. The first approach relies on the well-known Genetic Algorithm to generate a sequence of external controls, while the second approach uses a variant of Reinforcement Learning. The Genetic algorithm achieves excellent transmission fidelity at as short transmission times as Reinforcement Learning, surpassing the fidelities achieved by the latter method. Nevertheless, the Reinforcement Learning method offers robust control policies when the control pulses are noisy enough, owing to an imperfect timing of the pulses, deficient control devices, or other sources of phase decoherence. We present the regime where each method is best suited to control the transmission of arbitrary qubit states.
Abstract In recent years, control methods based on different optimisation techniques have shed light on the possibilities of processing information in many quantum systems. When exploring the transmission of quantum states, faster transmission times are mandatory to avoid the deleterious effects of multiple sources of decoherence that spoil the transmission process. In particular, using reinforcement learning (RL) to devise sequences of step-wise external controls provides good transfer policies at short transmission times. We present two approaches to control the transmission of quantum states in qubit chains using external controls to force the dynamical evolution of the chain state. The first approach relies on the well-known genetic algorithm (GA) to generate a sequence of external controls, while the second approach uses deep RL (DRL), specifically a deep Q-network. The GA achieves excellent transmission fidelity at as short transmission times as RL, surpassing the fidelities achieved by the latter method. Nevertheless, the RL method offers robust control policies when the control pulses are noisy enough, owing to an imperfect timing of the pulses, deficient control devices, or other sources of phase decoherence. We present the regime where each method is best suited to control the transmission of arbitrary qubit states.
Abstract Several tasks involving the determination of the time evolution of a system of solid state qubits require stochastic methods in order to identify the best sequence of gates and the time of interaction among the qubits. The major success of deep learning in several scientific disciplines has suggested its application to quantum information as well. Thanks to its capability to identify best strategy in those problems involving a competition between the short term and the long term rewards, reinforcement learning (RL) method has been successfully applied, for instance, to discover sequences of quantum gate operations minimizing the information loss. In order to extend the application of RL to the transfer of quantum information, we focus on Coherent Transport by Adiabatic Passage (CTAP) on a chain of three semiconductor quantum dots (QD). This task is usually performed by the so called counter-intuitive sequence of gate pulses. Such sequence is capable of coherently transfer an electronic population from the first to the last site of an odd chain of QDs, by leaving the central QD unpopulated. We apply a technique to find nearly optimal gate pulse sequence without explicitly give any prior knowledge of the underlying physical system to the RL agent. Using the advantage actor-critic algorithm, with a small neural net as function approximator, we trained a RL agent to choose the best action at every time step of the physical evolution to achieve the same results previously found only by ansatz solutions.
The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications.
The design of high-fidelity quantum gates is difficult because it requires the optimization of two competing effects, namely maximizing gate speed and minimizing leakage out of the qubit subspace. We propose a deep reinforcement learning algorithm that uses two agents to address the speed and leakage challenges simultaneously. The first agent constructs the qubit in-phase control pulse using a policy learned from rewards that compensate short gate times. The rewards are obtained at intermediate time steps throughout the construction of a full-length pulse, allowing the agent to explore the landscape of shorter pulses. The second agent determines an out-of-phase pulse to target leakage. Both agents are trained on real-time data from noisy hardware, thus providing model-free gate design that adapts to unpredictable hardware noise. To reduce the effect of measurement classification errors, the agents are trained directly on the readout signal from probing the qubit. We present proof-of-concept experiments by designing X and square root of X gates of various durations on IBM hardware. After just 200 training iterations, our algorithm is able to construct novel control pulses up to two times faster than the default IBM gates, while matching their performance in terms of state fidelity and leakage rate. As the length of our custom control pulses increases, they begin to outperform the default gates. Improvements to the speed and fidelity of gate operations open the way for higher circuit depth in quantum simulation, quantum chemistry and other algorithms on near-term and future quantum devices.