Behavior of the maximum likelihood in quantum state tomography

被引:14
|
作者
Scholten, Travis L. [1 ,2 ]
Blume-Kohout, Robin [1 ,2 ]
机构
[1] Sandia Natl Labs, CCR, Livermore, CA 94550 USA
[2] Univ New Mexico, Ctr Quantum Informat & Control CQuIC, Albuquerque, NM 87131 USA
来源
NEW JOURNAL OF PHYSICS | 2018年 / 20卷
基金
美国国家科学基金会;
关键词
quantum state tomography; model selection; compressed sensing;
D O I
10.1088/1367-2630/aaa7e2
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint rho >= 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) should not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.
引用
下载
收藏
页数:20
相关论文
共 50 条
  • [31] Quantum Maximum Likelihood Decoding for Linear Block Codes
    Jung, Hyunwoo
    Kang, Jeonghwan
    Ha, Jeongseok
    11TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE: DATA, NETWORK, AND AI IN THE AGE OF UNTACT (ICTC 2020), 2020, : 227 - 232
  • [32] Efficiency of maximum-likelihood reconstruction of quantum states
    Hradil, Z
    Rehácek, J
    FORTSCHRITTE DER PHYSIK-PROGRESS OF PHYSICS, 2001, 49 (10-11): : 1083 - 1088
  • [33] Least-squares and maximum-likelihood in computed tomography
    Grewar, Murdock G.
    Myers, Glenn R.
    Kingston, Andrew M.
    JOURNAL OF MEDICAL IMAGING, 2022, 9 (03)
  • [34] APPLICATION OF A MAXIMUM LIKELIHOOD ALGORITHM TO ULTRASOUND MODULATED OPTICAL TOMOGRAPHY
    Nam Trung Huynh
    He, Diwei
    Hayes-Gill, Barrie R.
    Crowe, John A.
    Walker, John G.
    Mather, Melissa L.
    Rose, Felicity R. A. J.
    Parker, Nicholas G.
    Povey, Malcolm J. W.
    Morgan, Stephen P.
    PHOTONS PLUS ULTRASOUND: IMAGING AND SENSING 2011, 2011, 7899
  • [35] Least-squares and maximum-likelihood in Computed Tomography
    Grewar, Murdock G.
    Myers, Glenn R.
    Kingston, Andrew M.
    DEVELOPMENTS IN X-RAY TOMOGRAPHY XIII, 2021, 11840
  • [36] ON THE PERFORMANCE OF MAXIMUM-LIKELIHOOD RECONSTRUCTIONS IN POSITRON EMISSION TOMOGRAPHY
    VERMEULEN, FL
    PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 1983, 397 : 274 - 279
  • [37] MAXIMUM LIKELIHOOD SURFACE PROFILOMETRY VIA OPTICAL COHERENCE TOMOGRAPHY
    Rapp, J.
    Mansour, H.
    Boufounos, P.
    Orlik, P.
    Koike-Akino, T.
    Parsons, K.
    2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, 2022, : 1561 - 1565
  • [38] Application of a maximum likelihood algorithm to ultrasound modulated optical tomography
    Huynh, Nam T.
    He, Diwei
    Hayes-Gill, Barrie R.
    Crowe, John A.
    Walker, John G.
    Mather, Melissa L.
    Rose, Felicity R. A. J.
    Parker, Nicholas G.
    Povey, Malcolm J. W.
    Morgan, Stephen P.
    JOURNAL OF BIOMEDICAL OPTICS, 2012, 17 (02)
  • [39] MAXIMUM-LIKELIHOOD-ESTIMATION OF OBJECT LOCATION IN DIFFRACTION TOMOGRAPHY
    DEVANEY, AJ
    TSIHRINTZIS, GA
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1991, 39 (03) : 672 - 682
  • [40] An accelerated penalized maximum likelihood algorithm for positron emission tomography
    Chang, Ji-Ho
    Anderson, John M. M.
    Mair, Bernard A.
    IEEE TRANSACTIONS ON NUCLEAR SCIENCE, 2007, 54 (05) : 1648 - 1659