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ÇѱÛÁ¦¸ñ(Korean Title) |
¾çÀÚ ±×·¡ÇÁ ÇÕ¼º°ö ½Å°æ¸Á ±â¹ÝÀÇ À¯±â ºÐÀÚ HOMOLUMO Gap ¿¹Ãø ¸ðµ¨ |
¿µ¹®Á¦¸ñ(English Title) |
Quantum Graph Convolutional Neural Network-Based Models for Predicting the HOMO-LUMO Gap of Organic Molecules |
ÀúÀÚ(Author) |
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Ju-Young Ryu
June-Koo Kevin Rhee
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¿ø¹®¼ö·Ïó(Citation) |
VOL 45 NO. 01 PP. 2426 ~ 2429 (2022. 06) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (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. |
Å°¿öµå(Keyword) |
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