Quantum Machine Learning

Presenting the results of my recent accepted paper, Modified Layerwise Learning for Data Re-uploading Classifier in High-Energy Physics Event Classification, at the Quantum Algorithms & Applications track and Quantum Computing for High-Energy Physics Workshop of IEEE Quantum Week 2021.

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.

Presenting the progress of my research on Quantum Generative Adversarial Networks algorithm to simulate the $t\bar{t}H(b\bar{b})$ production processes in the LHC experiment at the CERN Summer Student Session.

Improving quantum machine learning performance on high-energy physics event classification.

Presenting the progress of my work on Quantum Convolutional Neural Networks in classifying images of particles from high-energy physics events at the MCQST Student Conference.

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.

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.