{"id":578,"date":"2025-06-23T02:25:35","date_gmt":"2025-06-23T02:25:35","guid":{"rendered":"https:\/\/sites.rutgers.edu\/chong-sun\/?page_id=578"},"modified":"2025-10-29T22:27:36","modified_gmt":"2025-10-29T22:27:36","slug":"about-chong","status":"publish","type":"page","link":"https:\/\/sites.rutgers.edu\/sun-lab\/about-chong\/","title":{"rendered":"About Chong"},"content":{"rendered":"

\"\"Chong\u2019s research focuses on the interface between traditional quantum chemistry and emerging technologies such as artificial intelligence (AI) and quantum information. Her goal is to build a computational platform for understanding intricate chemical and physical phenomena and discovering novel functional materials to support the next wave of the industrial revolution. Chong received her B.A. from Peking University, under the supervision of Profs. Hong Jiang and Wenjian Liu, where she studied spin-crossover materials using density functional theory (DFT) and Monte Carlo simulations. She completed her Ph.D. at Caltech under Prof. Garnet Chan, where she developed classical and quantum algorithms for strongly correlated electrons, including finite-temperature density matrix embedding theory (FT-DMET) and the quantum imaginary time evolution (QITE) algorithm. After her Ph.D., Chong was a postdoctoral researcher with Prof. Al\u00e1n Aspuru-Guzik at the University of Toronto, where she developed machine learning models for chemical systems, including the neural network quantum state (NNQS) Waveflow and the autoregressive molecular generation model Quetzal. She later worked with Prof. Gustavo Scuseria at Rice University on traditional quantum chemistry methods, where she developed a framework for selected non-orthogonal configuration interaction with single and double excitations (SNOCISD). Chong also has industrial research experience as a scientist at Zapata AI Inc. and Microsoft, where she worked on quantum computing solutions to chemical problems.<\/p>\n

Teaching<\/strong><\/p>\n

2026 Spring<\/p>\n

CHEM 487\/542 – Special Topics in Physical Chemistry: Chemical Data Science<\/p>\n

Tentative syllabus can be found here<\/a>.<\/p>\n

Pre-Rutgers<\/p>\n

CHM 470\/1478 Statistical Mechanics <\/p>\n

Lecture Notes can be found here<\/a>.<\/p>\n

Education<\/strong><\/p>\n

Ph.D. California Institute of Technology (2021)
\nB.A. Peking University (2015)<\/p>\n

Talks<\/strong><\/p>\n

(1) \u201cDesigning Quantum Chemistry Methods with and Beyond Chemical Intuition.\u201d Harvard University, Cambridge, MA, Feb. 2025.<\/p>\n

(2) \u201cDesigning Quantum Chemistry Methods with and Beyond Chemical Intuition.\u201d Rutgers University, New Brunswick, NJ, Jan. 2025.<\/p>\n

(3) \u201cDesigning Quantum Chemistry Methods with and Beyond Chemical Intuition.\u201d SUNY Binghamton, Binghamton, NY, Dec. 2024.<\/p>\n

(4) \u201cDesigning Quantum Chemistry Methods with and Beyond Chemical Intuition.\u201d University of Oklahoma, Norman, OK, Dec. 2024.<\/p>\n

(5) \u201cDesigning Quantum Chemistry Methods with and Beyond Chemical Intuition.\u201d University of Rhode Island, Kingston, RI, Nov. 2024.<\/p>\n

(6) \u201cDesigning Computational Methods for Strongly Correlated Electrons.\u201d Rice Quantum Initiative (RQI) Seminar, Houston, TX, Nov. 2024.<\/p>\n

(7) \u201cHow to Design Quantum Chemistry Methods.\u201d Texas A&M University, College Station, TX, Oct. 2024.<\/p>\n

(8) \u201cNeural Networks as a Quantum Chemistry Ansatz: A Chemist\u2019s View.\u201d University of Washington, Seattle, WA, Aug. 2024.<\/p>\n

(9) \u201cStudy of Many-Body Localization with Complex Polarization.\u201d APS March Meeting, Mar. 2024.<\/p>\n

(10) \u201cA Chemist\u2019s View of Quantum Computing.\u201d Tulane University, Online, Nov. 2023.<\/p>\n

(11) \u201cNumerical Solutions to Large Eigenvalue Problems in Chemistry.\u201d Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, Mar. 2023.<\/p>\n

(12) \u201cStudy of Strongly Correlated Materials with Density Matrix Embedding Theory.\u201d 15th International Conference on Theoretical and High-Performance Computational Chemistry (ICT-HPCC22), Online, Jul. 2022.<\/p>\n

(13) \u201cDesigning Classical and Quantum Algorithms for Strongly Correlated Materials.\u201d Google Quantum, Online, Nov. 2021.<\/p>\n

(14) \u201cDensity Matrix Embedding Theory and Quantum Imaginary Time Evolution.\u201d ByteDance, Online, Aug. 2021.<\/p>\n

(15) \u201cSolving Chemistry Problems with Quantum Computers.\u201d Caltech CCE Seminar Day, Pasadena, CA, Nov. 2020.<\/p>\n

(16) \u201cQuantum Algorithms for Quantum Chemistry Simulations.\u201d Peking University, Beijing, China, Jan. 2020.<\/p>\n

(17) \u201cQuantum Imaginary Time Evolution.\u201d 23rd Annual Conference on Quantum Information Processing (QIP), Jan. 2020.<\/p>\n

(18) \u201cFinite-Temperature Density Matrix Embedding Theory.\u201d Simons Many-Electron Collaboration Summer School, Jun. 2019.<\/p>\n

(19) \u201cFinite-Temperature Density Matrix Embedding Theory.\u201d APS March Meeting, Mar. 2017.<\/p>\n","protected":false},"excerpt":{"rendered":"

Chong\u2019s research focuses on the interface between traditional quantum chemistry and emerging technologies such as artificial intelligence (AI) and quantum information. 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