Our group investigates topological features in optical systems to discover new physics and develop optical devices with built-in protection.
Recent advances in nanophotonic devices have enabled a variety of new technologies, including light-based classical information processing as a promising alternative to electronic signals in future circuits, non-classical light generation, and potential avenues for quantum information sciences. Our group aims to theoretically and experimentally investigate various quantum properties of light-matter interaction for applications in quantum information processing and sensing. Moreover, we explore associated fundamental phenomena, such as many-body physics, that could emerge in such physical systems. Our research is at the interface of quantum optics, condensed matter physics, and more recently, machine learning.
We explore many-body quantum dynamics of strongly interacting systems. Specifically, we investigate novel effects specific to optical systems, such as dissipative-driven phenomena.
We exploit hybrid approaches to probe and manipulate single-particle and many-body quantum states, such as optical manipulation of electronic topological states.
August 21, 2019
Dissipation induces decoherence in a quantum system which is usually detrimental for quantum state engineering and quantum information processing. However, by engineering dissipation properly, it can actually be a useful resource for preparing exotic quantum strongly-correlated states.
August 03, 2019
Topological protection allows realization of integrated photonic devices that are robust against certain fabrication-induced disorder. However, it is usually challenging to achieve device reconfigurability along with topological robustness.
June 18, 2019
The topological phases of matter are characterized using the Berry phase, a geometrical phase associated with the energy-momentum band structure.
September 11, 2018
A quantum light source has many potential applications in quantum information sciences. However, any on-chip realization is marred by the nanofabrication-induced disorder.
August 16, 2018
We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data.