Machine Learning and AI for Autonomous Identification of 2D Crystals

Introduction

In quantum machine field, detecting two-dimensional (2D) materials in Silicon chips is one of the most critical problems. It is considered as one of bottlenecks in quantum research because of time and labor consumptions spent on finding a potential flake that might be useful. This progress takes hours to finish without any warranty that detected flakes being helpful. In order to speedup, reduce cost and efforts of this progress, we leverage computer vision and AI to build an end-to-end system for automatically identifying potential flakes and exploring their charactersitics (e.g thickness).

2D Quantum Crystals Identification

We provide a flexible and generalized solution for 2D quantum crystals identification running on realtime with high accuracy. The algorithm is able to work with any kind of flakes (e.g hBN, Graphine, etc), hardware and environmental settings. It will help to reduces time and labor consumption in research of quantum technologies.

Quantum Neural Networks

Classical neural network algorithms are computationally expensive. For example, in image classification, representing an image pixel by pixel using classical information requires an enormous amount of computational memory resources. Hence, exploring methods to represent images in a different paradigm of information is important. We proposed a parameter encoding scheme for defining and training neural networks in quantum information based on time evolution of quantum spaces.

Differentiating between a classical neural network and a variational quantum circuit

Schematic of the Variational Quantum circuit used. Every layer is defined as a series of rotation gates in the x and y directions. Every qubit is linked to one other using CNOT gates.

Our Team

Advisors

Dr. Khoa Luu	Dr. Hugh Churchill
Assistant Professor	Professor
khoaluu@uark.edu	churchill@uark.edu

Gradudate

Xuan Bac Nguyen

Ph.D. Student

xnguyen@uark.edu

Under Gradudate

Apoorva Bisht	Jordan Simpson
Honors Student	Honors Student
abisht@uark.edu	jcs049@uark.edu

Publication

Xuan Bac Nguyen, Apoorva Bisht, Hugh Churchill, Khoa Luu
Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning
Under review of the IEEE International Conference in Image Processing. 2022.

@article{Nguyen2022TwoDimensionalQM,
               title={Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning},
               author={Xuan-Bac Nguyen and Apoorva Bisht and Hugh Churchill and Khoa Luu},
               journal={ArXiv},
               year={2022},
               volume={abs/2205.15948}
               }
               

Dendukuri, Aditya, Blake Keeling, Arash Fereidouni, Joshua Burbridge, Khoa Luu, and Hugh Churchill
Defining Quantum Neural Networks via Quantum Time Evolution
In Proceedings of Quantum Techniques in Machine Learning. 2019

@article{dendukuri2019defining,
               title={Defining quantum neural networks via quantum time evolution},
               author={Dendukuri, Aditya and Keeling, Blake and Fereidouni, Arash and Burbridge, Joshua and Luu, Khoa and Churchill,
               Hugh},
               journal={arXiv preprint arXiv:1905.10912},
               year={2019}
               }