Quantum computing, topological condensed matter, and quantum machine learning
I develop variational quantum eigensolver (VQE) frameworks for studying topological superconductor candidates, focusing on the Kitaev chain model. This work involves Jordan-Wigner mappings of the BdG Hamiltonian to qubit operators, computing topological invariants (winding numbers, entanglement entropy), and characterizing topological phase transitions including the effects of disorder. The goal is to leverage near-term quantum hardware to probe many-body topological phases that are computationally expensive classically.
I build machine learning pipelines that bridge quantum physics and data science. This includes CNN-based prediction of Majorana zero mode visibility from simulated nanowire conductance data, as well as quantum support vector machines (QSVM) and quantum neural networks (QNN) applied to real-world classification problems. I work with both PennyLane and Qiskit frameworks to explore quantum kernel methods and hybrid quantum-classical architectures.
I explore circuit synthesis techniques including Reed-Muller decoding-based Clifford+T optimization and cat qubit simulation for open quantum systems. This work contributes to the broader goal of making quantum algorithms more practical by reducing circuit depth and improving fault tolerance.
Much of my research leverages high-performance computing resources at Clemson's Palmetto cluster, including GPU-accelerated training of neural networks and large-scale parameter sweeps for phase diagram generation. I use SLURM for job scheduling, conda for environment management, and emphasize reproducibility through locked dependencies and seeded computations.
Selected research and hackathon projects
VQE framework for identifying topological superconductor candidates via the Kitaev chain. Includes real-space BdG Hamiltonian construction, Jordan-Wigner qubit mapping, and topological invariant computation.
GitHub →CNN pipeline predicting Majorana zero mode visibility from nanowire conductance data. Uses ResNet-18 architecture with PyTorch for training and inference on simulated datasets.
GitHub →QSVM, QNN, and Cirq-based quantum kernels for tornado prediction, developed for the SRNL Quantum Computing Challenge. Combines classical ML with quantum feature maps.
GitHub →Reed-Muller decoding-based Clifford+T circuit optimizer (rmsynth). Developed at MIT's annual quantum hackathon for efficient quantum circuit synthesis.
GitHub →Cat qubit simulation for the Alice & Bob open quantum systems challenge. Explored bosonic error correction codes and noise-biased qubit architectures.
GitHub →Hybrid QNN/QSVM system for EEG analysis built with PennyLane, featuring a Streamlit web interface. Developed at the NYU Abu Dhabi Quantum Hackathon for neurological disorder detection.
GitHub →The official Clemson Quantum Group website, built with Next.js, React, and TypeScript. Features group member profiles, research highlights, and news.
GitHub →Educational quantum circuit simulation activity designed to introduce quantum computing concepts through an interactive, hands-on exercise.
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