Manuel Guatto, Gian Antonio Susto, Francesco Ticozzi
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引用次数: 0
Abstract
Obtaining reliable state preparation protocols is a key step toward practical implementation of many quantum technologies, and one of the main tasks in quantum control. In this work, different reinforcement learning approaches are used to derive a feedback law for state preparation of a desired state in a target system. In particular, we focus on the robustness of the obtained strategies with respect to different types and amount of noise. Comparing the results indicates that the learned controls are more robust to unmodeled perturbations with respect to simple feedback strategy based on optimized population transfer, and that training on a simulated nominal model retains the same advantages displayed by controllers trained on real data. The possibility of effective off-line training of robust controllers promises significant advantages toward practical implementation.
期刊介绍:
Physical Review A (PRA) publishes important developments in the rapidly evolving areas of atomic, molecular, and optical (AMO) physics, quantum information, and related fundamental concepts.
PRA covers atomic, molecular, and optical physics, foundations of quantum mechanics, and quantum information, including:
-Fundamental concepts
-Quantum information
-Atomic and molecular structure and dynamics; high-precision measurement
-Atomic and molecular collisions and interactions
-Atomic and molecular processes in external fields, including interactions with strong fields and short pulses
-Matter waves and collective properties of cold atoms and molecules
-Quantum optics, physics of lasers, nonlinear optics, and classical optics