True experimental reconstruction of quantum states and processes via convex optimization

被引:0
|
作者
Akshay Gaikwad
Kavita Arvind
机构
[1] Indian Institute of Science Education and Research (IISER) Mohali,Department of Physical Sciences
来源
关键词
NMR quantum computing; Quantum state tomography; Quantum process tomography; Constrained convex optimization;
D O I
暂无
中图分类号
学科分类号
摘要
We use a constrained convex optimization (CCO) method to experimentally characterize arbitrary quantum states and unknown quantum processes on a two-qubit NMR quantum information processor. Standard protocols for quantum state and quantum process tomography are based on linear inversion, which often result in an unphysical density matrix and hence an invalid process matrix. The CCO method, on the other hand, produces physically valid density matrices and process matrices, with significantly improved fidelity as compared to the standard methods. We use the CCO method to estimate the Kraus operators and characterize gates in the presence of errors due to decoherence. We then assume Markovian system dynamics and use a Lindblad master equation in conjunction with the CCO method, to completely characterize the noise processes present in the NMR system.
引用
收藏
相关论文
共 50 条
  • [31] Continuous Ratio Optimization via Convex Relaxation with Applications to Multiview 3D Reconstruction
    Kolev, Kalin
    Cremers, Daniel
    CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4, 2009, : 1858 - 1864
  • [32] Statistical Inference via Convex Optimization
    Ghosh, Debashis
    INTERNATIONAL STATISTICAL REVIEW, 2020, 88 (03) : 806 - 808
  • [33] Covariance prediction via convex optimization
    Shane Barratt
    Stephen Boyd
    Optimization and Engineering, 2023, 24 : 2045 - 2078
  • [34] Learning the kernel via convex optimization
    Kim, Seung-Jean
    Zymnis, Argyrios
    Magnani, Alessandro
    Koh, Kwangmoo
    Boyd, Stephen
    2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, : 1997 - 2000
  • [35] Statistical Inference via Convex Optimization
    Ghosh, Debashis
    INTERNATIONAL STATISTICAL REVIEW, 2020,
  • [36] Covariance prediction via convex optimization
    Barratt, Shane
    Boyd, Stephen
    OPTIMIZATION AND ENGINEERING, 2023, 24 (03) : 2045 - 2078
  • [37] Sensor Selection via Convex Optimization
    Joshi, Siddharth
    Boyd, Stephen
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2009, 57 (02) : 451 - 462
  • [38] Robust polarimetry via convex optimization
    Leamer, Jacob M.
    Zhang, Wenlei
    Saripalli, Ravi K.
    Glasser, Ryan T.
    Bondar, Denys, I
    APPLIED OPTICS, 2020, 59 (28) : 8886 - 8894
  • [39] Experimental realization of controlled quantum teleportation of arbitrary qubit states via cluster states
    Kumar, Abhijeet
    Haddadi, Saeed
    Pourkarimi, Mohammad Reza
    Behera, Bikash K.
    Panigrahi, Prasanta K.
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [40] Experimental realization of controlled quantum teleportation of arbitrary qubit states via cluster states
    Abhijeet Kumar
    Saeed Haddadi
    Mohammad Reza Pourkarimi
    Bikash K. Behera
    Prasanta K. Panigrahi
    Scientific Reports, 10