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Team Name:
Lindwurm
Team Member:
Sangwoo Park ( shadow1229@naver.com / shadow1229@snu.ac.kr )
Project Description:
The goal of the project is building quantum machine learning-based classifiers which can classifies water molecules and sodium ions present in the crystallographic structure of protein obtained by X-ray crystallography, as a kind of toy program for predicting physicochemical properties related with the protein. X-ray crystallography is mainly used to obtain the structure of a protein with high resolution, by using diffraction of X-ray due to electrons in the protein. Due to the nature of the method, small molecules, atoms or ions with the same number of electrons are likely to produce similar peaks. For example, water molecule, one of the small molecules abaundant in protein crystal structures, have 10 electrons, is likely to be confused with sodium ions which has 10 electrons. However, since water molecules does not have net charge, while sodium ions having positive net charge, the structure of proteins that can hold water molecules and sodium ions are likely to be different. From this, water molecules and sodium ions in X-ray crystallography can be distinguished.
In this project, 2 quanvolutional neural network-based water-sodium ion classifier is presented, where the input is a voxelized 3D image of the structure of carbon, nitrogen, and oxygen atoms from proteins or other compounds (excluding water) in a cube which center is located at a location where sodium ion or water molecule exists and size of 16Å and grid spacing of 0.5Å. For quanvolution, two network was made, where one is a pair of non-trainable 4-qubit quantum circuits as pre-processing of the input and the other is pair of trainable 4-qubit quantum circuits in the last layer before fully connected layer of the classifier. For comparison, the performance of 2 quanvolutional neural network-based classifier was compared with the performance of CNN-based classifier.
Presentation:
https://github.com/shadow1229/Qhack_2022/blob/main/Quantum_sea/presentation.pdf
Source code:
https://github.com/shadow1229/Qhack_2022/tree/main/Quantum_sea
Which challenges/prizes would you like to submit your project for?
Bio-QML Challenge Quantum Chemistry Challenge Amazon Braket Challenge IBM Qiskit Challenge