My research focuses on making quantum computing practical, reliable, and scalable by connecting system-level optimization with quantum circuit and algorithm design. I study how quantum applications can be designed, evaluated, and executed more effectively on real and emerging quantum platforms, with interests spanning reliability and performance modeling, design automation, variational quantum algorithms, and quantum machine learning.
Building on this foundation, I am expanding toward distributed quantum computing and quantum computing for scientific discovery.
Performance and reliability modeling, workload management, runtime optimization, and noise-aware execution.
Explore →Quantum circuit design, automated optimization, variational quantum algorithms, and quantum machine learning.
Explore →Distributed and heterogeneous quantum systems, scalable quantum workflows, and emerging scientific applications.
Explore →Today's quantum computers operate under substantial noise, temporal variation, and limited hardware resources. My research develops system-level methods to characterize these behaviors and improve the reliability and efficiency of quantum application execution.
I am particularly interested in performance and reliability modeling, workload management, runtime optimization, and noise-aware execution. The broader goal is to understand how quantum systems behave in practice and use that knowledge to make application execution more predictable and effective.
Performance and reliability modeling for quantum systems operating under unstable noise.
System-level workload optimization for balancing fidelity and throughput on noisy quantum computers.
Effective quantum applications require coordinated decisions across algorithms, circuit structures, implementation strategies, and hardware constraints. My research investigates automated methods for navigating this design space and improving the effectiveness of quantum circuits and applications.
This work spans quantum design automation, variational quantum algorithms, quantum machine learning, and emerging approaches for fault-tolerant quantum circuit implementation. I am interested in connecting algorithm-level objectives with circuit- and system-level behavior rather than treating these layers independently.
Application-oriented variational quantum circuit design for quantum machine learning on NISQ platforms.
Design automation for integrating quantum error correction with variational quantum circuits.
Noise-aware quantum error correction and simulation methods for emerging fault-tolerant workflows.
As quantum computing systems grow in scale and complexity, I am interested in extending my work in workload management, performance modeling, and runtime optimization toward distributed and heterogeneous quantum computing. This includes questions surrounding multi-QPU execution, resource orchestration, heterogeneous quantum platforms, and scalable quantum workflows.
In parallel, I am exploring how quantum computing can contribute to scientific discovery. I am particularly interested in connecting quantum systems and algorithms with scientific workflows, including emerging applications in healthcare and life sciences.
These directions build naturally on my prior work in quantum workload management, performance and reliability modeling, and variational quantum applications.
I welcome conversations with researchers and students interested in quantum systems, design automation, distributed quantum computing, and scientific applications.
lij270@miamioh.edu