Instrumental distortions in quantum optimal control.

Area of Science:

• Quantum physics
• Quantum control engineering

Background:

• Quantum optimal control methods like GRAPE are crucial for manipulating quantum states.
• Instrumental distortions in quantum systems, unlike in magnetic resonance spectroscopy, can significantly impact control signals, leading to non-linearities and instability.
• Existing methods often struggle with these complex and time-varying distortions.

Purpose of the Study:

• To introduce a novel framework for quantum optimal control that accounts for instrumental distortions.
• To develop a method that produces robust quantum control sequences resilient to signal distortions.
• To integrate this framework into existing open-source quantum control software.

Main Methods:

• Developed a response-aware gradient ascent pulse engineering (GRAPE) framework.
• Integrated differentiable distortion models into the GRAPE optimization loop.
• The framework avoids the need for inverse filter functions.
• Implemented into the Spinach library (versions 2.10+).

Main Results:

• The new framework successfully accounts for cascades of differentiable distortions.
• It generates quantum control sequences that are resilient to specified distortion cascades and parameter variations.
• The method is robust against non-linear, unstable, and time-varying instrumental effects.

Conclusions:

• The response-aware GRAPE framework offers a significant advancement in robust quantum control.
• It enables precise manipulation of quantum states in the presence of complex instrumental distortions.
• This approach enhances the reliability and applicability of quantum optimal control in diverse experimental settings.