High-Performance Simulation of Physical Systems

Our work applies high-performance quantum simulation techniques to the study of physical systems, with an emphasis on models arising in condensed matter physics, chemistry, and related domains. These systems are typically described by Hamiltonians that encode the interactions and dynamics of many-body systems, forming the basis for understanding materials, molecular behavior, and complex physical processes. Quantum simulation provides a natural framework for studying these systems, but practical implementation requires tight integration with high-performance classical computing.

A central component of this research is the development of benchmarking and evaluation frameworks that enable systematic analysis of quantum simulation performance across both classical and quantum platforms. Our work contributes to application-oriented benchmarking approaches that measure fidelity, execution time, and scalability across representative Hamiltonian models, including systems such as the Ising, Heisenberg, and Hubbard models. These benchmarks provide a consistent methodology for comparing algorithms, simulators, and hardware, and for identifying the conditions under which quantum approaches may offer advantages.

We also investigate algorithmic techniques that improve the accuracy and efficiency of quantum simulation workflows, particularly in the computation of physical observables such as energy expectation values. This includes methods for reducing measurement overhead, optimizing circuit construction, and managing tradeoffs between accuracy and runtime. These improvements are essential for enabling meaningful simulations on current and near-term quantum systems, where resource constraints and noise remain limiting factors.

At larger scales, classical simulation remains a critical tool for validating quantum algorithms and exploring system behavior beyond current hardware limits. Our work leverages GPU-accelerated and multi-node HPC systems to simulate quantum circuits at scale, addressing challenges such as exponential memory growth and the performance impact of distributed data movement. Advances in multi-GPU architectures and interconnect technologies play an important role in enabling these simulations.

Building on this foundation, our ongoing research extends these methods toward more realistic and higher-fidelity simulations of physical systems. By combining scalable HPC techniques with quantum algorithm development and rigorous benchmarking, we aim to provide computational tools that support scientific discovery across disciplines and advance the practical application of quantum simulation in real-world research settings.