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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 future optoelectronic devices, 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
Group News
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February 27, 2020
From 2017, Google has been giving the award called the Google Noisy Intermediate-Scale Quantum (NISQ) award for the academic researchers who collaborated with Google's quantum computing development teams.
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September 30, 2019
The mechanism by which thermodynamics sets the direction of time’s arrow has long fascinated scientists.
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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.