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Resonance Fluorescence of an InGaAs Quantum Dot in a Planar Cavity Using Orthogonal Excitation and Detection

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Ray-based framework for state identification in quantum dot devices.

Justyna P Zwolak¹, Thomas McJunkin², Sandesh S Kalantre^3,4

¹National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.

PRX Quantum : a Physical Review Journal

|February 3, 2023

Summary

This summary is machine-generated.

A new ray-based classification (RBC) framework uses machine learning to efficiently identify quantum dot (QD) states for quantum computing. This method significantly reduces measurement time and points needed, improving scalability for complex qubit systems.

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Area of Science:

Quantum Computing
Quantum Dot Systems
Machine Learning Applications

Background:

Quantum dots (QDs) are a promising platform for scalable quantum computing.
Increasing qubit numbers lead to complex control parameter spaces.
Traditional measurement techniques (e.g., two-parameter scans) are impractical for large systems.

Purpose of the Study:

To develop a more efficient measurement technique for complex QD systems.
To introduce a machine learning-based classifier for automated QD state recognition.
To enable automated recognition of qubit-relevant parameter regimes in high-dimensional spaces.

Main Methods:

Introduction of the ray-based classification (RBC) framework.
Utilizing one-dimensional projections of device response in multidimensional parameter space.
Application of RBC as a machine learning classifier for QD states and an optimizer.

Main Results:

RBC achieved accuracy surpassing the 82% benchmark of image-based techniques.
Reduced measurement points by up to 70%, significantly cutting measurement time.
Demonstrated effective tuning to multiqubit regimes in 2D and 3D parameter spaces.

Conclusions:

RBC offers an efficient solution for state identification and optimization in high-dimensional QD systems.
This machine learning approach is crucial for overcoming scalability challenges in quantum computing hardware.
Experimental validation confirms the efficacy of RBC for non-traditional, time-intensive quantum measurements.