Our group investigates topological features in optical systems to discover new physics and develop optical devices with built-in protection.
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Machine learning in quantum systems
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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.
Research Areas
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.
Group News
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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.
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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.
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June 18, 2019
The topological phases of matter are characterized using the Berry phase, a geometrical phase associated with the energy-momentum band structure.
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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.
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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.