Research Overview
My research connects quantum algorithm design to statistically grounded, hardware-facing evaluation. A recurring theme is to combine uncertainty quantification, resource estimation, compilation-aware benchmarking, and characterization workflows to understand how circuit structure, transpilation overhead, backend noise, resource cost, and model assumptions affect quantum algorithms in practice.
- Algorithms
- Quantum Signal Processing and Quantum Singular Value Transformation (QSP/QSVT), Green's-function methods, molecular spectroscopy, VQAs, and open quantum dynamics.
- Evaluation
- Uncertainty quantification, Bayesian posterior estimation, sensitivity analysis, resource estimation, and Markovian QCVV-informed characterization (Quantum Characterization, Verification, and Validation).
- Systems
- Noise-aware benchmarking, transpilation reuse, bottleneck analysis, and validation across near-term and emerging fault-tolerant regimes.
Research Areas
Organized into clusters spanning algorithm design, prior ML/network-science background, cost-aware evaluation, statistical validation, and hardware characterization.
Quantum Algorithms
- Quantum Singular Value Transformation (QSVT)
- Quantum Signal Processing (QSP)
- Hamiltonian Simulation
- Green's-function-based Molecular Spectroscopy
- Computational Open Quantum Systems Dynamics
- Variational Quantum Algorithms
- Quantum Machine Learning
- LLM-Assisted Quantum Code Translation
- Fault-Tolerant Quantum Algorithms
Prior Machine Learning & Network Science
- Graph Neural Networks
- Network Science for Structured Data
Resource Estimation & Compilation
- Resource Estimation for Quantum Algorithms
- Computational Bottleneck Identification
- Compilation-Aware Quantum Benchmarking
- Transpilation Reuse for Large Quantum Circuits
Uncertainty Quantification & Statistics
- Uncertainty Quantification
- MCMC-Based Bayesian Posterior Estimation
- Sensitivity Analysis
Characterization & Visualization
- Understanding of Markovian QCVV Protocols for Benchmarking
- Quantum Error Mitigation
- Benchmarking and Characterization of Noisy Quantum Systems
- Quantum Tomography and Randomized Benchmarking Protocols
- Quantum Noise Visualization
Selected Research Themes
QSVT / QSP Quantum Algorithms
Quantum algorithm design using Quantum Signal Processing (QSP) and Quantum Singular Value Transformation (QSVT) for Hamiltonian simulation in both real and imaginary time, Green's-function-based molecular spectroscopy, and open quantum system dynamics.
Resource Estimation and Bottleneck Analysis
Cost-aware analysis of quantum algorithms using circuit width, depth, gate counts, two-qubit interactions, and scaling behavior to identify computational bottlenecks.
Compilation-Aware Benchmarking
Workflow-level efficiency techniques, including transpiling large quantum algorithm circuits once and reusing the compiled circuit to reduce repeated transpilation overhead.
Uncertainty Quantification for NISQ
Bayesian posterior estimation, sensitivity analysis, and statistically grounded reliability characterization for noisy quantum workloads.
Variational Quantum Algorithms
VQE, QAOA, variational linear solvers, metrology, channel fidelity estimation, gate set tomography, compilation, PDEs, and error-correction-oriented workflows for benchmarking.
Scalable Quantum Noise Visualization
Visual analytics for interpretable diagnosis of circuit-, qubit-, and backend-level error structure.
Research Experience
PhD Research Intern
Pacific Northwest National Laboratory, Richland, WA
Working on QSP/QSVT-based approaches for molecular spectroscopy and open quantum dynamics.
Focus areas include block-encoding-based algorithm design, noise-aware evaluation, and hardware-relevant analysis of quantum simulation workflows.
Graduate Research Assistant
Kent State University, Kent, OH
Conducting doctoral research across quantum algorithms, compilation-aware benchmarking, and noise characterization of NISQ hardware. Continues concurrently with the PNNL appointment.
Projects span QSP/QSVT and Hamiltonian simulation, resource estimation and computational bottleneck identification, transpilation reuse for large quantum circuits, MCMC-based Bayesian posterior estimation and sensitivity analysis, applying Markovian QCVV protocols for noise characterization, quantum error mitigation, and visualization of noise on quantum systems.