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| 한글제목(Korean Title) |
양자 그래프 합성곱 신경망 기반의 유기 분자 HOMOLUMO Gap 예측 모델 |
| 영문제목(English Title) |
Quantum Graph Convolutional Neural Network-Based Models for Predicting the HOMO-LUMO Gap of Organic Molecules |
| 저자(Author) |
류주영
이준구
Ju-Young Ryu
June-Koo Kevin Rhee
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| 원문수록처(Citation) |
VOL 45 NO. 01 PP. 2426 ~ 2429 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
This study presents a quantum machine learning model for predicting HOMO-LUMO gaps of organic molecules. This model represents molecules as graphs and encodes them as quantum states. A quantum graph convolutional neural network with single-qubit gates on all qubits and two qubit gates determined by the bonds in the molecule is used. Various quantum circuits were tested, and topics for future work are discussed. |
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| 파일첨부 |
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