The pros and cons of using deep reinforcement learning or genetic algorithms to design control schemes for quantum state transfer on qubit chains
Sof\'ia Per\'on Santana, Ariel Fiuri, Mart\'in Dom\'inguez, and Omar Osenda

TL;DR
This paper compares deep reinforcement learning and genetic algorithms for controlling quantum state transfer in qubit chains, highlighting their respective strengths in fidelity and robustness under noise.
Contribution
It introduces two control methods for quantum state transfer and analyzes their performance, revealing the conditions where each method excels.
Findings
Genetic algorithms achieve high fidelity at short transmission times.
Reinforcement learning provides more robust control under noisy conditions.
Both methods outperform traditional approaches in specific regimes.
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…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Spectroscopy and Quantum Chemical Studies
