Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems

Abstract

We deploy a combination of reinforcement learning-based approaches and more traditional optimization techniques to identify optimal protocols for population transfer in a multi-level system. We constraint our strategy to the case of fixed coupling rates but time-varying detunings, a situation that would simplify considerably the implementation of population transfer in relevant experimental platforms, such as semiconducting and superconducting ones. Our approach is able to explore the space of possible control protocols to reveal the existence of efficient protocols that, remarkably, differ from (and can be superior to) standard Raman, STIRAP or other adiabatic schemes. The new protocols that we identify are robust against both energy losses and dephasing.

References in corpus (8)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Demonstration of Two-Qubit Algorithms with a Superconducting Quantum Processor
• Quantum information processing with circuit quantum electrodynamics
• Superadiabatic population transfer in a three-level superconducting circuit
• Model-Free Quantum Control with Reinforcement Learning
• Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
• Quantum Architecture Search via Deep Reinforcement Learning
• Population transfer in a Lambda system induced by detunings

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