Quantum Technology
Enhancing Quantum Computer Readout
Using a variety of simulation techniques, our researchers are modeling superconducting
qubit noise to predict the readout noise, optimize the pulse width, energy, and resonator
design for high-fidelity readout. Below is a rotating selection of our standout investigators
in this subject matter.
○ Recent News and Highlights
○ Related Strength: Semiconductors
Selected Publications
Asplund, C., et al., (2015). Holographic entanglement entropy from 2d CFT: Heavy states and local quenches. Journal of High Energy Physics (2).
Hurst, H. M. & Flebus, B. (2022). Non-Hermitian physics in magnetic systems. Journal of Applied Physics, 132.
Wang, X., Khatami, E., Fei. F, et al., (2022). Experimental Realization of an Extended Fermi-Hubbard Model Using a 2D Lattice of Dopant-based Quantum Dots. Silver Nature Communications, 13.
Wong, H. (2025). Quantum Computing Architecture and Hardware for Engineers - Step by Step. Springer International Publishing.
Wong, H. (2024). Introduction to Quantum Computing: From a Layperson to a Programmer in 30 Steps, Second Edition. Springer International Publishing.
Award Highlights
Betre, Asplund, "A Transformative Masters Program in HighEnergy Physics" — DOE, 2023
Chiao, Khatami, “MRI: Acquisition of Hybrid CPU/GPU High Performance Computing and Storage for STEM Research and Education at SJSU” — NSF, 2016
Hurst, Khatami, Wong, “Collaborative Research: NRT-QL: A Program for Training a Quantum Workforce” — NSF, 2021
Johnson, Khatami (Co-PI), “Artificial Intelligence and Data Science Enabled Predictive Modeling of Collective Phenomena in Strongly Correlated Quantum Materials” — DOE, 2024
Keleş, "CAREER: Multi-scale mechanical behavior of quantum dot nanocomposites: Towards data-driven automatic discovery of high-performance structures” — NSF, 2022
Wong, "Collaborative Research: Elements: Empowering Semiconductor Device Research and Education through Integrated Machine Learning Models and Database" — NSF, 2024
Wong, "Cryogenic Characterization and Modeling of MST Devices and Analog Circuits Augmented with TCAD-enabled Machine Learning" — Atomera, Inc., 2024
Innovations
Variable Channel Doping in Vertical Transistor [pdf]
A practical and efficient solution for enhancing the performance of power transistors.
Featured Faculty
Curtis Asplund
Assistant Professor of Physics and Astronomy
High Energy Theoretical Physics, Entanglement Entropy and Complexity of Quantum Field
Theories and Black Holes, Applications of Gauge/Gravity Duality to Condensed Matter
Systems
ORCID: 0000-0003-0557-5850
Kassahun Betre
Assistant Professor of Physics and Astronomy
High-Energy Theory, Quantum Gravity, Theoretical Particle Physics
ORCID: 0000-0003-1063-5870
Hilary M Hurst
Assistant Professor of Physics and Astronomy
Quantum Physics, Quantum Control, Quantum Information Science, Ultracold Gases, Bose-Einstein
Condensation, Spinor Condensate, Weak Measurement, Quantum Measurement
ORCID: 0000-0002-7197-7615
Ehsan Khatami
Professor of Physics and Astronomy
Quantum Many-Body Physics, Quantum simulations, Condensed Matter Physics, Numerical
Methods, Machine Learning
ORCID: 0000-0003-4256-6232
Hiu Yung Wong
Professor of Electrical Engineering
TCAD, Quantum Computing, Superconducting Qubit, Simulation Augmented Machine Learning,
Cryogenic Electronics, (Ultra) Wide Bandgap Device
ORCID: 0000-0003-0135-7469
Potential collaborators and members of the media may contact us at officeofresearch@sjsu.edu.
