Quantum computing harnesses many unique properties of quantum mechanics, such as quantum entanglement and superposition, offering distinct capabilities that exceed those of conventional computing apparatus. Our overarching objective is to devise efficient quantum algorithms tailored to address scientific computational issues, particularly those that present formidable challenges to classical computers.
Hamiltonian simulation algorithms involve constructing a quantum circuit that performs the unitary transformation U associated with a Hamiltonian operator H. They are important building blocks for many other quantum computing algorithms. Our work focuses on Hamiltonian simulations for multi-scale quantum dynamics [4] and stochastic implementations of quantum dynamics in the second quantization form.
In practice, quantum systems are inevitably subject to environmental noise. We design both quantum and classical algorithms to effectively simulate the behavior of these open quantum systems. In addition, we develop mathematical models for quantum dynamics outside the Markovian regime.
Quantum systems can be guided to perform specific tasks or maximize certain physical properties. We designed quantum algorithms for QOC problems with precise error bound and complexity estimates.
First-principle calculations in material science eliminate the need for empirical assumptions and possess the potential to predict material properties with quantum-level precision. The aim of our projects is to employ quantum algorithms in these first-principle computations, enabling the consideration of significantly larger systems and facilitating a direct connection between electronic structures and macroscale behavior.
We create quantum algorithms specifically designed to solve partial differential equations, particularly in situations where the numerical discretization results in a high degree of freedom.
Markov chains play a central role in modern data science algorithms. We construct algorithms that mix a Markov chain with quantum speedup.
QML uses parameterizing quantum circuits to approximate unknown functions. Our focus is to analyze the role of measurement noise in the learning procedure.
Professor, Penn State.
Graduate Student.
Graduate Student.
Graduate Student.
Undergraduate Student.