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
  • [1] True experimental reconstruction of quantum states and processes via convex optimization
    Gaikwad, Akshay
    Arvind
    Dorai, Kavita
    QUANTUM INFORMATION PROCESSING, 2021, 20 (01)
  • [2] Reconstruction of quantum channel via convex optimization
    Huang, Xuan-Lun
    Gao, Jun
    Jiao, Zhi-Qiang
    Yan, Zeng-Quan
    Zhang, Zhe-Yong
    Chen, Dan-Yang
    Zhang, Xi
    Ji, Ling
    Jin, Xian-Min
    SCIENCE BULLETIN, 2020, 65 (04) : 286 - 292
  • [3] Quantum error correction via convex optimization
    Robert L. Kosut
    Daniel A. Lidar
    Quantum Information Processing, 2009, 8 : 443 - 459
  • [4] Quantum error correction via convex optimization
    Kosut, Robert L.
    Lidar, Daniel A.
    QUANTUM INFORMATION PROCESSING, 2009, 8 (05) : 443 - 459
  • [5] Defining quantum divergences via convex optimization
    Fawzi, Hamza
    Fawzi, Omar
    QUANTUM, 2021, 5
  • [6] Robust quantum error correction via convex optimization
    Kosut, Robert L.
    Shabani, Alireza
    Lidar, Daniel A.
    PHYSICAL REVIEW LETTERS, 2008, 100 (02)
  • [7] Convex Optimization for Nonequilibrium Steady States on a Hybrid Quantum Processor
    Lau, Jonathan Wei Zhong
    Lim, Kian Hwee
    Bharti, Kishor
    Kwek, Leong-Chuan
    Vinjanampathy, Sai
    PHYSICAL REVIEW LETTERS, 2023, 130 (24)
  • [8] Reconstruction of quantum states by applying an analytical optimization model
    Prasad, Rohit
    Ghosh, Pratyay
    Thomale, Ronny
    Huber-Loyola, Tobias
    PHYSICAL REVIEW A, 2025, 111 (02)
  • [9] Sparse Resource Allocation for Control of Spreading Processes via Convex Optimization
    Somers, Vera L. J.
    Manchester, Ian R.
    IEEE CONTROL SYSTEMS LETTERS, 2021, 5 (02): : 547 - 552
  • [10] Fast Quantum State Reconstruction via Accelerated Non-Convex Programming
    Kim, Junhyung Lyle
    Kollias, George
    Kalev, Amir
    Wei, Ken X. X.
    Kyrillidis, Anastasios
    PHOTONICS, 2023, 10 (02)