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Nathan Eli Miller

  • About Me
  • Research
  • Relevant Experience
  • Awards

Research

Publications

  • N. E. Miller, Biswadeep Chakraborty and Saibal Mukhopadhyay, “A Reconfigurable Quantum State Tomography Solver in FPGA,” 2023 IEEE International Conference on Quantum Computing and Engineering (2023).
  • N. E. Miller and Saibal Mukhopadhyay, “Quantum State Tomography in FPGA,” Optica Quantum 2.0 Conference and Exhibition, paper QTu3A.1 (2023).
  • N. E. Miller and Saibal Mukhopadhyay, “A Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor,” Sci Rep 11, 23391 (2021).
  • N. E. Miller, Zheng Wang, Saurabh Dash, Asif Islam Khan and Saibal Mukhopadhyay, “Impact of HKMG and FDSOI FeFET drain current variation in processing-in-memory architectures,” Journal of Materials Research (2021).
    • MS Thesis published under the same title by N. E. Miller (2022).
  • N. E. Miller, Zheng Wang, Saurabh Dash, Asif Islam Khan and Saibal Mukhopadhyay, “Characterization of Drain Current Variations in FeFETs for PIM-based DNN Accelerators,” 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS).
  • N. E. Miller, “Simulating the Effects of Mechanical Stress on Hall-Effect Sensor Devices,” Undergraduate Thesis in UF Libraries (2019).

Talks

  • “Quantum-Classical Hardware Co-Design Towards Useful Quantum Computers,” 2024 Air Force Office of Scientific Research Joint Review for Atomic and Molecular Physics and Quantum Information Science Portfolios, Arlington, VA, May 2024.
  • “Quantum-Classical Co-Design Towards Useful Quantum Computers,” Sandia National Laboratories, Albuquerque, NM (Virtual), Apr. 2024.
  • “Feedback-based calibration for tuning and drift control of a trapped-ion quantum processor,” APS March Meeting 2024, Minneapolis, MN, Mar. 2024.
  • “Real-Time Feedback for Calibration of Quantum Processors,” MIT Lincoln Laboratories, Lexington, MA, Feb. 2024.
  • Beyond-CMOS Computing in the National Interest
    US Army Research Laboratory Adelphi Laboratory Center, Adelphi, MD, Nov. 2023
  • Feedback-Based Calibration for Rapid Tuning and Drift Control of Quantum Processors
    Assessing the Performance of Quantum Computers (APQC) Workshop, Estes Park, CO, Sept. 2023
  • Accelerated Quantum State Tomography with Machine Learning Applications
    National Defense Science and Engineering Graduate Fellows Conference, San Antonio, TX, Aug. 2023
  • Quantum State Tomography in FPGA
    Optica Quantum 2.0 Conference and Exhibition, Denver, CO, July 2023.
  • An Introduction to Quantum Machine Learning
    Georgia Institute of Technology ECE8863: Quantum Computing Devices and Hardware, Atlanta, GA, Apr. 2023
  • Characterization of Drain Current Variations in FeFETs for PIM-Based DNN Accelerators
    IEEE AICAS Conference, Virtual, USA, June 2021
  • Impact of FeFET Drain Current Variation in Processing-in-Memory Architectures
    Materials Research Society Spring Meeting, Virtual, USA, April 2021

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My CV as of March, 2024

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