Projects

Quantum Convolutional Neural Networks for High-Energy Physics Analysis at the LHC
A Google Summer of Code 2021 Project Repository. This project aims to demonstrate quantum machine learning’s potential, specifically Quantum Convolutional Neural Network (QCNN), in HEP events classification from particle image data. The code used in the research is wrapped as an open-source package to ease future research in this field.
Go to Project Site GitHub v1.0.0 Docs Tutorial MIT License
Quantum Convolutional Neural Networks for High-Energy Physics Analysis at the LHC
Event Classification with Layerwise Learning for Data Re-uploading Classifier in High-Energy Physics
This project aims to use modified layerwise learning on data re-uploading classifier to classify events in HEP. The project won second place at Xanadu’s QHack Quantum Machine Learning Open Hackathon 2021.
GitHub
Event Classification with Layerwise Learning for Data Re-uploading Classifier in High-Energy Physics
Quantum Image Classifier Design with Data Re-uploading Quantum Convolution and Data Re-uploading Classifier Scheme
Undergraduate Thesis Project.
This research proposed a modification scheme of the VQA-based Data Reuploading Classifier (DRC) and a DRC-based quantum convolution scheme for MNIST classification. The proposals achieved an improvement in accuracy compared to the previous related works.
GitHub
Quantum Image Classifier Design with Data Re-uploading Quantum Convolution and Data Re-uploading Classifier Scheme
Nightmode Photography: Exposure Fusion
Implementation of night mode photography algorithm utilizing histogram equalization, image registration, and Mertens exposure fusion.
GitHub
Nightmode Photography: Exposure Fusion
Quantum Distance-based Classifier
This is a code implementation of distance-based classifier (similar to k-nearest neighbour algorithm) using quantum computer adapted from research paper "Implementing a distance-based classifier with a quantum interference circuit" by Maria Schuld, Mark Fingerhuth, and Francesco Petruccione.
GitHub
Quantum Distance-based Classifier
Learning Human Touch Interaction with Convolutional Neural Networks
This project is the works done during my research internship at Gentiane Venture’s Laboratory, Tokyo Univ. of Agriculture and Technology, Japan. The project is about using CNN to classify several types of touch interaction from humans by learning the data pattern from a force sensor.
GitHub
Learning Human Touch Interaction with Convolutional Neural Networks