Quantum Machine Learning and Autonomous 2D Crystals Identification

CVIU Lab, Department of Computer Science and Computer Engineering
Department of Physics
University of Arkansas
Email: khoaluu@uark.edu

Quantum Machine Learning

1. 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

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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.

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2. Unsupervised Clustering by Quantum Machine

Clustering problems are computationally expensive, especially for large datasets. In addition, it requires a significant amount of computational resource and time to solve in the typical computer. Meanwhile, quantum computing can perform calculations exponentially faster than classical computer. Also, it is suitable for solving complex optimization and searching problems. By this reason, quantum computing comes in as a potential solution to accelerate clustering algorithms and improve their performance.
By this reasons, we prose a novel unsupervised clustering framework ultilizing quantum computer. In particular, we formuate cluster as an optimization problem and then leverage quantum computer for speedup clustering process.

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3. Solving Transportation and Logistics Problems with Quantum

The Vehicle Routing Problem (VPR) is to find the best routs for a fleet of vehicles to start from and return to a depot supplying the demand of all customers by visiting them once. The objective of this problem is to minimize the total cost of travel with the contraint of vehicles capacity.

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2D Quantum Crystals Identification

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).

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.

The cloud-based system overall

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Our Team


Dr. Khoa Luu Dr. Hugh Churchill
Assistant Professor
(Point of Contact)
khoaluu@uark.edu churchill@uark.edu


Xuan Bac Nguyen James Holidays
Ph.D. Student Ph.D. Student
xnguyen@uark.edu jbhollid@uark.edu

Under Gradudate

Apoorva Bisht Jordan Simpson Benjamin Thompson
Honors Student Honors Student Honors Student
abisht@uark.edu jcs049@uark.edu bct019@uark.edu


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.

               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},

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

               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,
               journal={arXiv preprint arXiv:1905.10912},