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SUMMARY:Wellness Day - No Quantum Lunch scheduled
DESCRIPTION:
URL:https://quantum.ncsu.edu/ibm-quantum/event/wellness-day-no-quantum-lunch-scheduled/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210319T120000
DTEND;TZID=America/New_York:20210319T130000
DTSTAMP:20260429T154019
CREATED:20210304T195127Z
LAST-MODIFIED:20210317T201525Z
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SUMMARY:Quantum Lunch Series Event - Speaker Milad Marvian
DESCRIPTION:Talk Title: Quantum Earth Mover’s Distance: A New Approach to Learning Quantum Data \nSpeaker: Professor Milad Marvian\, University of New Mexico \nMilad Marvian is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of New Mexico and also a member of the Center for Quantum Information and Control (CQuIC). He completed his Ph.D. in 2018 at the University of Southern California and before joining UNM was a postdoctoral associate at MIT. His research interest includes quantum algorithms and quantum machine learning\, quantum error correction\, and open quantum systems. \nAbstract:\nIn this talk\, I will introduce a generalization of the earth mover’s distance to the set of quantum states. The proposed distance recovers the Hamming distance for the vectors of the canonical basis\, and more generally the classical earth mover’s distance for quantum states diagonal in the canonical basis. I will discuss some desirable properties of this distance\, including a continuity bound for the von Neumann entropy and its insensitivity to local perturbations\, and I will show how these properties make the distance suitable for learning quantum data using quantum generative adversarial networks\nBased on https://arxiv.org/abs/2009.04469 and https://arxiv.org/abs/2101.03037. \n  \nThis is a Virtual Event. \nFor more information email: \nquantumcomputing@ncsu.edu
URL:https://quantum.ncsu.edu/ibm-quantum/event/quantum-lunch-series-event-speaker-milad-marvian/
ORGANIZER;CN="IBM Quantum Hub at NC State":MAILTO:quantumcomputing@ncsu.edu
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DTSTART;TZID=America/New_York:20210326T120000
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CREATED:20210216T175807Z
LAST-MODIFIED:20210322T130359Z
UID:1076-1616760000-1616763600@quantum.ncsu.edu
SUMMARY:Quantum Lunch Series Event - Speaker Mekena Metcalf
DESCRIPTION:Talk Title: Quantum Algorithms and Reinforcement Learning for Open Quantum System Simulation \nSpeaker:  Dr. Mekena Metcalf\, is a Postdoctoral Fellow in the Computational Research Division at Lawrence Berkeley National Laboratory. \nShe received her Ph.D. from the University of California\, Merced. Dr. Metcalf was the first graduate student in Prof. Chih-Chun Chien’s theory group\, and she was in the first cohort of doctoral students in the UC Merced Physics Department. Dr. Metcalf has published numerous theory papers relating to the simulation of quantum matter. In graduate school\, she devised methods to detect topological states of ultra-cold atoms and demonstrated novel properties of non-equilibrium fermion dynamics. After receiving her doctoral degree\, Dr. Metcalf began developing quantum algorithms at Berkeley Lab. She has developed original quantum algorithms for open-quantum system dynamics and fermion simulation. Dr. Metcalf now leads a team of scientists and engineers developing artificial intelligence algorithms to enhance quantum network and computing control. She is currently the Principal Investigator on a quantum transduction project and a Quantum System Accelerator Project Lead for robust quantum control. Dr. Metcalf is the first postdoc in Computing Sciences to become a Principal Investigator on an LDRD project. She is a member of the Diversity\, Equity\, and Inclusion working group within the Quantum System Accelerator. Along with her scientific research\, Dr. Metcalf is committed to making an inclusive and diverse quantum technology ecosystem a reality. \nAbstract: \nUndesired interactions with the environment make current quantum technology noisy. Characterizing\, modeling\, and simulating these system-environment interactions is challenging due to the macroscopic nature of the environment. We designed a quantum algorithm that uses time-dependent auxiliary qubits to mimic a macroscopic environment. The algorithm prepares finite-temperature states of general Hamiltonians; we benchmarked the algorithm performance using the Transverse Field Ising Model.  Our algorithmic approach offers a path towards open-quantum system simulation using quantum computers. \nIn parallel to quantum algorithm development\, we are developing deep reinforcement learning algorithms (DRL) to optimally control quantum systems in the presence of an environment. Artificial intelligence can enhance this efficiency by bridging experimental data and theoretical models. Our DRL algorithms are designed to enhance the efficiency of optomechanical transduction. We are building a device simulator to train the DRL algorithm for a proof-of-principle demonstration. I will discuss current developments and prospects for our quantum artificial intelligence research efforts. \n  \nThis is a Virtual Event. \nFor more information email: \nquantumcomputing@ncsu.edu
URL:https://quantum.ncsu.edu/ibm-quantum/event/quantum-lunch-series-event-speaker-mekena-metcalf/
ORGANIZER;CN="IBM Quantum Hub at NC State":MAILTO:quantumcomputing@ncsu.edu
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