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Atlanta, GA | Posted: April 10, 2021
Jackie Nemeth
School of Electrical and Computer Engineering
404-894-2905
jackie.nemeth@ece.gatech.edu
Summary Sentence:
ECE Ph.D. student Nathan Miller has been chosen as a recipient of the 2021 National Defense Science and Engineering Graduate (NDSEG) Fellowship.
Full Summary:
ECE Ph.D. student Nathan Miller has been chosen as a recipient of the 2021 National Defense Science and Engineering Graduate (NDSEG) Fellowship.
Nathan MillerNathan Miller has been chosen as a recipient of the 2021 National Defense Science and Engineering Graduate (NDSEG) Fellowship. This fellowship is the highest honor awarded to graduate students by the U.S. Department of Defense agencies. It will support Miller’s Ph.D. studies for three years in the Georgia Tech School of Electrical and Computer Engineering (ECE).
Miller has been a member of the Gigascale Reliable Energy-Efficient Nanosystem (GREEN) Lab since fall 2019 and graduated with his B.S.E.E. degree from the University of Florida. He is advised by Saibal Mukhopadhyay, who is the Joseph M. Pettit Professor in ECE and director of the GREEN Lab.
The title of Miller's research project is “Quantum Machine Learning for Dynamical Systems Considering Quantum Errors.” The objective is to explore the co-design of quantum devices and circuits for robust quantum Hopfield networks (QHNs), which are tolerant to quantum errors. In the noisy intermediate-scale quantum (NISQ) computing era, quantum computers are very limited in size and are highly susceptible to noise.
Using a quantum neuron design, Miller and his colleagues have implemented a quantum Hopfield associative memory (QHAM) architecture using IBM’s quantum development platform IBMQ, which can be run on actual quantum hardware devices. They benchmark the system performance against errors from decoherence of the quantum bits (qubits), limited connectivity in hardware, and quantum circuit errors from measurement and quantum gate implementation.
With this groundwork, Miller and his colleagues are developing reliable QHNs, which will facilitate efficient machine enabled learning of complex dynamical systems. By maximizing the computation power of a limited number of qubits, significant contributions to solving difficult machine learning problems can be created through quantum computing in the NISQ era.
Applications of Miller’s research include solving computationally difficult problems in areas such as quantum chemistry, biological systems, disease propagation, weather forecasting, multi-agent robotics, and computer vision. Areas impacted through these models include medicine, climate science, autonomous systems, security, and economics, and have broad, global impacts which can contribute greatly to everyday quality of life.
