Deep learning parameter estimation and quantum control of single molecule

TL;DR

This paper introduces machine learning methods to accurately estimate physical parameters of a single molecule under quantum control at room temperature, enhancing the design of coherent control protocols.

Contribution

It develops and compares optimization and neural network approaches for parameter estimation from spectroscopic signals in quantum control experiments.

Findings

Neural network approach achieves high accuracy in parameter inference.

Both methods effectively estimate system-bath interactions at room temperature.

Robust parameter estimation improves quantum control protocol design.

Abstract

Coherent control, a central concept in physics and chemistry, has sparked significant interest due to its ability to fine-tune interference effects in atoms and individual molecules for applications ranging from light-harvesting complexes to molecular qubits. However, precise characterization of the system's dissipative dynamics is required for its implementation, especially at high temperature. In a quantum control experiment, this means learning system-bath parameters and driving coupling strengths. Here, we demonstrate how to infer key physical parameters of a single molecule driven by spectrally modulated pulses at room temperature. We develop and compare two computational approaches based on two-photon absorption photoluminescence signals: an optimization-based minimization scheme and a feed-forward neural network. The robustness of our approach highlights the importance of reliable parameter estimation in designing effective coherent control protocols. Our results have direct applications in ultrafast spectroscopy, quantum materials and technology.