Quantum state reconstruction for systems with different dimensions using machine learning

POSTER

Abstract

A machine-learning-based reconstruction system trained exclusively on m qubits is presented here to reconstruct a quantum state on systems of n qubits where m>n. This method eliminates the need to match the dimension of the system with the dimension of a model used for training. Additionally, we use the monotonicity property of the fidelity to relate the average reconstruction fidelity of m qubits to any lower-dimensional n qubits. We reconstruct the quantum states of randomly sampled one, two, and three qubit systems with a machine-learning model trained on four qubit systems. This approach provides a robust time-efficient machine-learning-based quantum state tomography as we reduce time required for training a model.

*Work by S. Lohani and T. A. Searles was supported in part by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA) under contract number DE-SC0012704. A portion of this work was performed at Oak Ridge National Laboratory, operated by UT-Battelle for the U.S. Department of Energy under contract no. DE-AC05-00OR22725. J. M. Lukens acknowledges funding by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, through the Early Career Research Program (Field Work Proposal ERKJ353). Additionally, this material is based upon work supported by, or in part by, the Army Research Laboratory and the Army Research Office under contract/grant numbers W911NF-19-2-0087 and W911NF-20-2-0168.

Presenters

  • Sangita Regmi

    • University of Illinois Chicago

Authors

  • Sangita Regmi

    • University of Illinois Chicago

  • Sanjaya Lohani

    • University of Illinois Chicago

  • Joseph M Lukens

    • Oak Ridge National Laboratory

  • Ryan T Glasser

    • Tulane Univ

  • Brian T Kirby

    • DEVCOM Army Research Lab
    • DEVCOM Army Research Laboratory

  • Thomas A Searles

    • University of Illinois at Chicago