Ray-based classification framework for high-dimensional data

Abstract

While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than using dense, multi-dimensional data, we propose a deep neural network (DNN) classification framework that utilizes a minimal collection of one-dimensional representations, called \emph{rays}, to construct the "fingerprint" of the structure(s) based on substantially reduced information. We empirically study this framework using a synthetic dataset of double and triple quantum dot devices and apply it to the classification problem of identifying the device state. We show that the performance of the ray-based classifier is already on par with traditional 2D images for low dimensional systems, while significantly cutting down the data acquisition cost.

References in corpus (8)

• Quantum Computation with Quantum Dots
• Machine learning and the physical sciences
• Scalable gate architecture for densely packed semiconductor spin qubits
• An electrostatically defined serial triple quantum dot charged with few electrons
• Machine Learning techniques for state recognition and auto-tuning in quantum dots
• A 2x2 quantum dot array with controllable inter-dot tunnel couplings
• QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
• Counting the learnable functions of structured data

Cited by in corpus (2)

• Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays
• Theoretical bounds on data requirements for the ray-based classification