Machine Learning Optimization of Majorana Hybrid Nanowires

Area of Science:

• Quantum computing
• Condensed matter physics
• Machine learning

Background:

• Increasing complexity of quantum systems necessitates automated tuning.
• Quantum bit arrays and Majorana wires present significant tuning challenges.
• Disorder effects can destroy crucial quantum phenomena.

Purpose of the Study:

• Investigate machine learning for automated tuning of quantum gate arrays.
• Apply the covariance matrix adaptation evolution strategy (CMA-ES) to Majorana wires.
• Assess the algorithm's ability to improve topological signatures and mitigate disorder.

Main Methods:

• Utilized machine learning, specifically CMA-ES, for tuning quantum gate arrays.
• Focused on Majorana wires as a case study with strong intrinsic disorder.
• Optimized gate voltages to recover quantum properties.

Main Results:

• The CMA-ES algorithm efficiently improved topological signatures.
• The algorithm successfully learned intrinsic disorder profiles within the quantum system.
• Complete elimination of disorder effects was achieved, recovering Majorana zero modes.
• Full recovery of Majorana zero modes was possible with only 20 optimized gates.

Conclusions:

• Machine learning-based tuning is a viable and efficient approach for complex quantum systems.
• CMA-ES can effectively overcome disorder in quantum systems like Majorana wires.
• Automated tuning strategies are crucial for advancing quantum computing hardware.