2020/12/05 by Onur Danaci, Sanjaya Lohani, Danaci, Onur +5 · 1 citation
Computer Science · Physics and Astronomy · #Quantum Information and Cryptography #Atomic and Subatomic Physics Research #Quantum Computing Algorithms and Architecture
paper · pdf · doi:10.48550/arxiv.2012.03104
Two-qubit systems typically employ 36 projective measurements for\nhigh-fidelity tomographic estimation. The overcomplete nature of the 36\nmeasurements suggests possible robustness of the estimation procedure to\nmissing measurements. In this paper, we explore the resilience of\nmachine-learning-based quantum state estimation techniques to missing\nmeasurements by creating a pipeline of stacked machine learning models for\nimputation, denoising, and state estimation. When applied to simulated\nnoiseless and noisy projective measurement data for both pure and mixed states,\nwe demonstrate quantum state estimation from partial measurement results that\noutperforms previously developed machine-learning-based methods in\nreconstruction fidelity and several conventional methods in terms of resource\nscaling. Notably, our developed model does not require training a separate\nmodel for each missing measurement, making it potentially applicable to quantum\nstate estimation of large quantum systems where preprocessing is\ncomputationally infeasible due to the exponential scaling of quantum system\ndimension.\n