Quantum Kernel Support Vector Machine for Quantum Dot State Recognition
TL;DR
This study evaluated state recognition from 100 by 100 two-gate current maps using 30 by 30 labeled patches under a strict device-level split to support fidelity-based quantum kernels as practical components for automated quantum dot tuning pipelines requiring few-shot generalization.
Abstract
: Semiconductor quantum dot platforms require rapid recognition of charge states during automated device tuning, especially when labeled data are scarce and device-to-device variation is strong. This study evaluated state recognition from 100 by 100 two-gate current maps using 30 by 30 labeled patches under a strict device-level split. Patch features were transformed with a signed logarithmic scale, standardized, compressed to four principal components, and scaled to the interval from zero to two pi. A fidelity quantum kernel support vector machine implemented in IBM Qiskit with a four-qubit ZZFeatureMap was compared against linear and radial basis function support vector machines across few-shot budgets of 10, 20, 40, and 80 samples per class. At 80 samples per class, the quantum kernel model achieved accuracy and macro F1 near 0.947 on held-out devices, outperforming the classical baselines in the evaluated setting. Noise-injection experiments showed stable macro F1 under perturbation. These findings support fidelity-based quantum kernels as practical components for automated quantum dot tuning pipelines requiring few-shot generalization.