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ABOUT

I am an incoming PhD student at MIT physics, formerly from Caltech. I'm interested in quantum computing, machine learning, and high energy theory.

INVITED TALKS

  • "Quantum machine learning for near-term applications.” Purdue University, 2021.
  • "A deep learning model for noise prediction on near-term quantum devices.” Cambridge Quantum Computing (CQC), August 2020.
  • "Novel machine learning algorithms for quantum annealing with applications in high energy physics.” Quantum Techniques in Machine Learning, Korea Advanced Institute of Science and Technology (KAIST), October 2019.
  • "Machine learning applications of quantum annealing in high energy physics.” AI-at-SLAC Seminar, Stanford Linear Accelerator Center, August 2019. (Abstract here.)

SELECTED PUBLICATIONS

See my CV for a full list of publications.

  • Entangling quantum generative adversarial networks.
    M. Niu*, A. Zlokapa*, M. Broughton, M. Mohseni, V. Smelyanskyi, and H. Neven, APS March Meeting, arXiv:2105.00080 [quant-ph], 2021. (Available here.)
  • A deep learning model for noise prediction on near-term quantum devices.
    A. Zlokapa and A. Gheorghiu, arXiv:2005.10811 [quant-ph], 2020. (Submitted. Available here.) Oral presentation, IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis, 2019. 1st place, ACM SRC SC19. Invited talk, Cambridge Quantum Computing.
  • Boundaries of quantum supremacy via random circuit sampling.
    A. Zlokapa, S. Boixo and D. Lidar, arXiv:2005.02464 [quant-ph], 2020. (Submitted. Available here.)
  • Quantum adiabatic machine learning by zooming into a region of the energy surface.
    A. Zlokapa, A. Mott, J. Job, J.-R. Vlimant, D. Lidar and M. Spiropulu, Physical Review A, 2020, 102 (6), 62405-62413. https://doi.org/10.1103/PhysRevA.102.062405. Invited talks, Stanford SLAC and QTML conference.
  • Graph neural networks for particle reconstruction in high energy physics detectors.
    X. Ju, A. Zlokapa, et al., 33rd Annual Conference on Neural Information Processing Systems (NeurIPS), Machine Learning for Physical Sciences Workshops, 2019. (Available here.)

AWARDS

Post-graduate fellowships:

  • Hertz Fellow
  • DoD NDSEG Fellow
  • Marshall Scholarship (declined)
  • NSF Graduate Research Fellowship (declined)
  • Stanford Knight-Hennessy Scholarship (declined)

Selected undergraduate awards:

  • Barry M. Goldwater Scholarship
  • [Caltech] Richard Feynman Prize in Theoretical Physics
  • [Caltech] Housner Prize (research)
  • [Caltech] Green Memorial Prize (research)
  • [Caltech] Haren Lee Fisher Memorial Award (physics)

TEACHING

  • Caltech Thomas A. Tisch Prize for Undergraduate Teaching in Computing and Mathematical Sciences (2021)
  • "Introduction to quantum machine learning," Caltech freshman physics seminar (2020)
  • Instructor, INQNET/Caltech, quantum computing summer program (2020, 2021)
  • TA, Ph101 Order-of-Magnitude Physics (2021)
  • TA, ACM95a Introductory Methods of Applied Mathematics for the Physical Sciences (2021)
  • TA, CS156b Learning Systems (article, 2020, 2021)

MISCELLANEOUS

Here are a few links to projects I did recently:

  • Machine learning forecasts of COVID-19 (project page and article).
  • Causal AI policymaker for malaria prevention at the Citadel Data Open (article).