Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays

Abstract

In spin based quantum dot arrays, material or fabrication imprecisions affect the behaviour of the device, which must be taken into account when controlling it. This requires measuring the shape of specific convex polytopes. In this work, we present an algorithm that automatically discovers count, shape and size of the facets of a convex polytope from measurements. Results on simulated devices as well as a real 2x2 spin qubit array show that we can reliably find the facets of the convex polytopes, including small facets with sizes on the order of the measurement precision.

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Cited by in corpus (6)

• Colloquium: Advances in automation of quantum dot devices control
• Autonomous estimation of high-dimensional Coulomb diamonds from sparse measurements
• Theoretical bounds on data requirements for the ray-based classification
• Robust quantum dots charge autotuning using neural network uncertainty
• Automated in situ optimization and disorder mitigation in a quantum device
• Learning Coulomb Diamonds in Large Quantum Dot Arrays