dorsal/arxiv
View SchemaThe pros and cons of using deep reinforcement learning or genetic algorithms to design control schemes for quantum state transfer on qubit chains
| Authors | Sofía Perón Santana, Ariel Fiuri, Martín Domínguez, Omar Osenda |
|---|---|
| Categories | |
| ArXiv ID | 2601.06303vv1 |
| URL | https://arxiv.org/abs/2601.06303 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
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.
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"abstract": "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.",
"arxiv_id": "2601.06303",
"authors": [
"Sof\u00eda Per\u00f3n Santana",
"Ariel Fiuri",
"Mart\u00edn Dom\u00ednguez",
"Omar Osenda"
],
"categories": [
"quant-ph"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "The pros and cons of using deep reinforcement learning or genetic algorithms to design control schemes for quantum state transfer on qubit chains",
"url": "https://arxiv.org/abs/2601.06303",
"version": "v1"
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