Deep Learning Parameter Estimation and Quantum Control of Single Molecules

Area of Science:

• Quantum physics and physical chemistry
• Coherent control of molecular systems

Background:

• Coherent control leverages quantum interference for applications like light-harvesting and molecular qubits.
• Accurate characterization of system-bath dynamics, especially at high temperatures, is essential for implementing coherent control.
• Quantum control experiments require learning system-bath parameters and driving coupling strengths.

Purpose of the Study:

• To demonstrate inference of key physical parameters for a single molecule under room-temperature conditions.
• To compare computational approaches for parameter estimation in quantum control.

Main Methods:

• Utilized two-photon absorption photoluminescence signals from a single molecule.
• Developed and compared an optimization-based minimization scheme and a feed-forward neural network for parameter inference.
• Applied spectrally modulated pulses for driving the molecule.

Main Results:

• Successfully inferred key physical parameters of a single molecule at room temperature.
• Demonstrated the robustness of the developed computational approaches.
• Highlighted the importance of reliable parameter estimation for effective coherent control protocols.

Conclusions:

• The developed computational methods provide a robust way to infer molecular parameters for quantum control.
• Accurate parameter estimation is vital for designing effective coherent control strategies.
• Results have direct implications for ultrafast spectroscopy, quantum materials, and quantum technology.