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I'm an undergraduate student at the California Institute of Technology, studying quantum computing and high energy physics and often working at the intersection of machine learning and physics.


  • I interned at the Google Quantum AI Lab under the supervision of Hartmut Neven to build new artificial intelligence models and physics simulation algorithms for quantum hardware, including the recent Google Sycamore device that showed quantum supremacy.
  • I'm currently developing error mitigation methods for quantum circuits at the Institute of Quantum Information and Matter at Caltech, using machine learning to create an intelligent compiler for the IBM Q quantum computers.
  • I proposed and developed energy correction and clustering components in an end-to-end deep learning pipeline for neutrino experiments (DUNE and MicroBooNE) with the DeepLearnPhysics collaboration at Stanford, using semantic segmentation and graph neural networks.
  • I demonstrated how deep learning can find particle tracks in the Large Hadron Collider at CERN under the HEP.Trkx project using graph neural networks.
  • I proposed a new quantum machine learning algorithm for Higgs boson classification on the D-Wave quantum computer, as well as quantum annealing for particle tracking at the Large Hadron Collider.
  • I simulated black hole collisions and implemented Bayesian methods for detecting the astrophysical gravitational wave background at LIGO.
  • I developed genetic algorithm optimization for neutron imaging analysis of scintillating crystals at the Berkeley Space Sciences Lab, publishing our results in a peer-reviewed paper.


  • "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.)


  • 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.)
  • 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 with zooming.
    A. Zlokapa, A. Mott, J. Job, J.-R. Vlimant, D. Lidar and M. Spiropulu, arXiv:1908.04480 [quant-ph], 2019. (Submitted. Available here.)
  • Charged particle tracking with quantum annealing-inspired optimization.
    A. Zlokapa, A. Anand, J.-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu, arXiv:1908.04475 [quant-ph], 2019. (Submitted. Available here.)
  • Machine learning methods for event reconstruction with liquid argon time projection chamber data.
    A. Zlokapa, K. Terao, H. Tanaka, and M. Spiropulu, DUNE Document, 2019. (Under submission.)
  • Optimizing Monte Carlo event generation using evolutionary computing techniques.
    A. Zlokapa, J.-R. Vlimant, and M. Spiropulu, CERN CMS/IN/Simulation, 2019.
  • Large-scale distributed training applied to generative adversarial networks for calorimeter simulation.
    J.-R. Vlimant, F. Pantaleo, M. Pierini, V. Loncar, S. Vallecorsa, D. Anderson, T. Nguyen, and A. Zlokapa, EPJ Web of Conferences, 2019, 214 (06025).
  • In-Situ Observation of Phase Separation During Growth of Cs2LiLaBr6:Ce Crystals Using Energy-Resolved Neutron Imaging.
    A. Tremsin, D. Perrodin, A. Losko, S. Vogel, T. Shinohara, K. Oikawa, J. Peterson, C. Zhang, J. Derby, A. Zlokapa, G. Bizarii and E. Bourret, Crystal Growth & Design, 2017, 17 (12), 6372-6381.


  • Graph neural networks for particle reconstruction in high energy physics detectors.
    X. Ju, A. Zlokapa, S. Farrell, J.-R. Vlimant, L. Gray, P. Calafiura, and M. Spiropulu, 33rd Annual Conference on Neural Information Processing Systems, Machine Learning for Physical Sciences Workshops, December 2019.
  • A deep learning approach to noise prediction and circuit optimization for near-term quantum devices.
    A. Zlokapa and A. Gheorghiu, IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis, November 2019. Best student paper, ACM SRC.


My CV contains the full list, but here are some highlights:

  • Haren Lee Fisher Memorial Award (outstanding undergraduate physics at Caltech)
  • Green Prize (outstanding undergraduate research at Caltech)
  • Barry M. Goldwater Scholar
  • 2nd place, ACM Student Research Competition Grand Finals (out of 22 conferences)
  • 1st place, ACM Student Research Competition, Supercomputing 2019
  • 1st place ($100,000), Citadel Data Open International Championship (and 1st in West Coast)
  • Hacktech (MLH@Caltech): Best Machine Learning Hack, Best Hardware Hack, Best IoT Hack
  • 2nd place, Intel International Science & Engineering Fair
  • US Presidential Scholar Candidate (perfect SAT score)
  • National Merit Scholar
  • Minor Planet 34134 Zlokapa (MIT Lincoln Lab)
  • American Invitational Mathematics Exam qualifier


I founded and am president of the Caltech Data Science Organization. Within the first month, we raised >$10,000 in corporate sponsorships; in the last 12 months, we have won $150,000 of prizes. In the club and my free time, I enjoy working on projects including machine learning tools to fight malaria, a virtual reality prototype for safer AI-powered firearms, supersonic rocket simulations for the FAR-MARS Prize, and deep learning for music composition.