Automated In Situ Optimization and Disorder Mitigation in a Quantum Device.

Area of Science:

• Quantum electronics
• Mesoscopic physics
• Materials science

Background:

• Quantum point contacts (QPCs) are fundamental building blocks in quantum electronics.
• Precise control over the potential landscape in QPCs is crucial for quantum device performance.
• Disorder in the material can significantly degrade the quantum conductance properties of QPCs.

Purpose of the Study:

• To develop and demonstrate an automated in situ optimization method for the potential landscape in QPCs.
• To improve the 'steplike' nature of conductance during channel constriction.
• To mitigate the effects of random disorder potentials on QPC conductance.

Main Methods:

• Utilized a 3x3 gate array patterned atop the QPC constriction for voltage control.
• Employed the covariance matrix adaptation evolutionary strategy for optimization.
• Introduced a metric to quantify the 'steplike' conductance behavior.
• Performed simulations using tight-binding models and conducted experiments on a physical device.

Main Results:

• Successfully optimized the gate voltages in both simulations and experiments.
• Demonstrated mitigation of random disorder potentials via in situ tuning.
• Observed a marked improvement in the quantization of conductance in the optimized QPC.

Conclusions:

• Automated in situ optimization is an effective strategy for enhancing QPC performance.
• The developed method can significantly improve conductance quantization and reduce disorder effects.
• This approach offers a pathway for more robust and controllable quantum electronic devices.