Limitations on Quantum Dimensionality Reduction

被引:0
|
作者
Harrow, Aram W. [1 ,2 ]
Montanaro, Ashley [3 ]
Short, Anthony J. [3 ]
机构
[1] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA
[2] Univ Bristol, Dept Math, Bristol BS8 1TW, Avon, England
[3] Univ Cambridge, Ctr Quantum Informat & Fdn, DAMTP, Cambridge, England
基金
英国工程与自然科学研究理事会;
关键词
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The Johnson-Lindenstrauss Lemma is a classic result which implies that any set of n real vectors can be compressed to O(log n) dimensions while only distorting pairwise Euclidean distances by a constant factor. Here we consider potential extensions of this result to the compression of quantum states. We show that, by contrast with the classical case, there does not exist any distribution over quantum channels that significantly reduces the dimension of quantum states while preserving the 2-norm distance with high probability. We discuss two tasks for which the 2-norm distance is indeed the correct figure of merit. In the case of the trace norm, we show that the dimension of low-rank mixed states can be reduced by up to a square root, but that essentially no dimensionality reduction is possible for highly mixed states.
引用
收藏
页码:86 / 97
页数:12
相关论文
共 50 条
  • [31] Transferred Dimensionality Reduction
    Wang, Zheng
    Song, Yangqiu
    Zhang, Changshui
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PART II, PROCEEDINGS, 2008, 5212 : 550 - 565
  • [32] General quantum matrix exponential dimensionality-reduction framework based on block encoding
    Li Y.-M.
    Liu H.-L.
    Pan S.-J.
    Qin S.-J.
    Gao F.
    Wen Q.-Y.
    Physical Review A, 2023, 108 (04)
  • [33] Consensus Clustering for Dimensionality Reduction
    Rani, D. Sandhya
    Rani, T. Sobha
    Bhavani, S. Durga
    2014 SEVENTH INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING (IC3), 2014, : 148 - 153
  • [34] Dimensionality reduction under scrutiny
    Yang, Yang
    Tuong, Zewen K.
    Yu, Di
    NATURE COMPUTATIONAL SCIENCE, 2023, 3 (01): : 8 - 9
  • [35] Dimensionality reduction for image retrieval
    Wu, P
    Manjunath, BS
    Shin, HD
    2000 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL III, PROCEEDINGS, 2000, : 726 - 729
  • [36] Discriminative Unsupervised Dimensionality Reduction
    Wang, Xiaoqian
    Liu, Yun
    Nie, Feiping
    Huang, Heng
    PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), 2015, : 3925 - 3931
  • [37] Dimensionality reduction under scrutiny
    Yang Yang
    Zewen K. Tuong
    Di Yu
    Nature Computational Science, 2023, 3 : 8 - 9
  • [38] Nonlinear dimensionality reduction for clustering
    Tasoulis, Sotiris
    Pavlidis, Nicos G.
    Roos, Teemu
    PATTERN RECOGNITION, 2020, 107 (107)
  • [39] Comparing Dimensionality Reduction Techniques
    Nick, William
    Shelton, Joseph
    Bullock, Gina
    Esterline, Albert
    Asamene, Kassahun
    IEEE SOUTHEASTCON 2015, 2015,
  • [40] Robust linear dimensionality reduction
    Koren, Y
    Carmel, L
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2004, 10 (04) : 459 - 470