About Chong

Chong’s 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. Alán 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.

Teaching

2026 Spring

CHEM 487/542 – Special Topics in Physical Chemistry: Chemical Data Science

Tentative syllabus can be found here.

Pre-Rutgers

CHM 470/1478 Statistical Mechanics

Lecture Notes can be found here.

Education

Ph.D. California Institute of Technology (2021)
B.A. Peking University (2015)

Talks

(1) “Designing Quantum Chemistry Methods with and Beyond Chemical Intuition.” Harvard University, Cambridge, MA, Feb. 2025.

(2) “Designing Quantum Chemistry Methods with and Beyond Chemical Intuition.” Rutgers University, New Brunswick, NJ, Jan. 2025.

(3) “Designing Quantum Chemistry Methods with and Beyond Chemical Intuition.” SUNY Binghamton, Binghamton, NY, Dec. 2024.

(4) “Designing Quantum Chemistry Methods with and Beyond Chemical Intuition.” University of Oklahoma, Norman, OK, Dec. 2024.

(5) “Designing Quantum Chemistry Methods with and Beyond Chemical Intuition.” University of Rhode Island, Kingston, RI, Nov. 2024.

(6) “Designing Computational Methods for Strongly Correlated Electrons.” Rice Quantum Initiative (RQI) Seminar, Houston, TX, Nov. 2024.

(7) “How to Design Quantum Chemistry Methods.” Texas A&M University, College Station, TX, Oct. 2024.

(8) “Neural Networks as a Quantum Chemistry Ansatz: A Chemist’s View.” University of Washington, Seattle, WA, Aug. 2024.

(9) “Study of Many-Body Localization with Complex Polarization.” APS March Meeting, Mar. 2024.

(10) “A Chemist’s View of Quantum Computing.” Tulane University, Online, Nov. 2023.

(11) “Numerical Solutions to Large Eigenvalue Problems in Chemistry.” Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, Mar. 2023.

(12) “Study of Strongly Correlated Materials with Density Matrix Embedding Theory.” 15th International Conference on Theoretical and High-Performance Computational Chemistry (ICT-HPCC22), Online, Jul. 2022.

(13) “Designing Classical and Quantum Algorithms for Strongly Correlated Materials.” Google Quantum, Online, Nov. 2021.

(14) “Density Matrix Embedding Theory and Quantum Imaginary Time Evolution.” ByteDance, Online, Aug. 2021.

(15) “Solving Chemistry Problems with Quantum Computers.” Caltech CCE Seminar Day, Pasadena, CA, Nov. 2020.

(16) “Quantum Algorithms for Quantum Chemistry Simulations.” Peking University, Beijing, China, Jan. 2020.

(17) “Quantum Imaginary Time Evolution.” 23rd Annual Conference on Quantum Information Processing (QIP), Jan. 2020.

(18) “Finite-Temperature Density Matrix Embedding Theory.” Simons Many-Electron Collaboration Summer School, Jun. 2019.

(19) “Finite-Temperature Density Matrix Embedding Theory.” APS March Meeting, Mar. 2017.