quMeas
quMeas is a high-performance, multi-threaded, quantum computing library that I developed in hybrid Python C++. By combining randomized measurement with cumulant expansion, it provides a powerful toolset to compute expectation values of long Pauli string operators from quantum measurement outputs. It employs a layered approach to parallelization that maximizes CPU utilization, allowing it to scale with high efficiencies.
QuEmb
At MIT, I spearheaded the development of QuEmb, an advanced Python framework designed to implement quantum embedding methods for simulating molecules and solids. In this project, I designed algorithms in imple- menting various numerical techniques and optimized performance. The implementation was modularized to streamline each numerical steps in the simulations.
CtrlQ
CtrlQ is a quantum computing toolkit that I independently developed at Virginia Tech. Written in hybrid Python C++, it simulates a device-level quantum state preparation on Transmon qubit devices using analog control pulses. It implements various numerical techniques and optimization algorithms. The codebase is highly modular and optimized for performance.
Fragpy
Fragpy is a Python tool that I developed during my graduate school for molecular geometry optimization. It implemented numerical optimization techniques to miminze molecular geometries. I also developed and implemented a multi-node parallelization approach to distribute computation by leveraging Python's multiprocessing and an automated interactive control module for node communication.
Molepy
During my graduate studies, I independently developed Molepy, an ab initio quantum chemistry program. The program implements highly computationally intensive numerical techniques to compute integrals for evaluating electron-electron interactions in quantum chemistry. The codebase is written in hybrid Python and C++, with performance critical modules implemented in C++.