A team of researchers from Oak Ridge National Laboratory, the University of Tennessee and Hong Kong Baptist University developed a new workflow that combines advances in automated chemical synthesis and machine-learning techniques.
ORNL’s next major computing achievement could open a new universe of scientific possibilities accelerated by the primal forces at the heart of matter and energy.
Using existing experimental and computational resources, a multi-institutional team has developed an effective method for measuring high-dimensional qudits encoded in quantum frequency combs, which are a type of photon source, on a single optical chip.
Gang Seob “GS” Jung has known from the time he was in middle school that he was interested in science.
Daniel Claudino studies the software development of libraries that enable large-scale quantum-classical heterogeneous computations. In this role, Daniel targets quantum simulations of interest in quantum chemistry.
Two years after ORNL provided a model of nearly every building in America, commercial partners are using the tool for tasks ranging from designing energy-efficient buildings and cities to linking energy efficiency to real estate value and risk.
Ali Passian studies the physics of sensing and imaging. In this role, Ali explores how new or better sensors can be developed to enable higher sensitivity, detection limit, and resolution for studying complex materials.
Warren Grice studies aspects of quantum information with a focus on optical approaches. In this role, Warren is involved in several projects aimed at transitioning the capabilities of quantum information to real-world applications.
John Serafini studies integrated photonics for developing secure quantum communications hardware. In this role, John simulates, designs and tests quantum-based photonic integrated circuits.
Vicente Leyton Ortega studies the potential of near-term quantum computing hardware for scientific applications, the performance of quantum and classical algorithms including optimization and machine learning