Mutually-orthogonal unitary and orthogonal matrices
- URL: http://arxiv.org/abs/2309.11128v1
- Date: Wed, 20 Sep 2023 08:20:57 GMT
- Title: Mutually-orthogonal unitary and orthogonal matrices
- Authors: Zhiwei Song, Lin Chen and Saiqi Liu
- Abstract summary: We show that the minimum and maximum numbers of an unextendible maximally entangled bases within a real two-qutrit system are three and four, respectively.
As an application in quantum information theory, we show that the minimum and maximum numbers of an unextendible maximally entangled bases within a real two-qutrit system are three and four, respectively.
- Score: 6.9607365816307
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce the concept of n-OU and n-OO matrix sets, a collection of n
mutually-orthogonal unitary and real orthogonal matrices under Hilbert-Schmidt
inner product. We give a detailed characterization of order-three n-OO matrix
sets under orthogonal equivalence. As an application in quantum information
theory, we show that the minimum and maximum numbers of an unextendible
maximally entangled bases within a real two-qutrit system are three and four,
respectively. Further, we propose a new matrix decomposition approach, defining
an n-OU (resp. n-OO) decomposition for a matrix as a linear combination of n
matrices from an n-OU (resp. n-OO) matrix set. We show that any order-d matrix
has a d-OU decomposition. As a contrast, we provide criteria for an order-three
real matrix to possess an n-OO decomposition.
Related papers
- Entrywise error bounds for low-rank approximations of kernel matrices [55.524284152242096]
We derive entrywise error bounds for low-rank approximations of kernel matrices obtained using the truncated eigen-decomposition.
A key technical innovation is a delocalisation result for the eigenvectors of the kernel matrix corresponding to small eigenvalues.
We validate our theory with an empirical study of a collection of synthetic and real-world datasets.
arXiv Detail & Related papers (2024-05-23T12:26:25Z) - Semi-supervised Symmetric Non-negative Matrix Factorization with Low-Rank Tensor Representation [27.14442336413482]
Semi-supervised symmetric non-negative matrix factorization (SNMF)
We propose a novel SNMF model by seeking low-rank representation for the tensor synthesized by the pairwise constraint matrix.
We then propose an enhanced SNMF model, making the embedding matrix tailored to the above tensor low-rank representation.
arXiv Detail & Related papers (2024-05-04T14:58:47Z) - Matrix decompositions in Quantum Optics: Takagi/Autonne,
Bloch-Messiah/Euler, Iwasawa, and Williamson [0.0]
We present four important matrix decompositions commonly used in quantum optics.
The first two of these decompositions are specialized versions of the singular-value decomposition.
The third factors any symplectic matrix in a unique way in terms of matrices that belong to different subgroups of the symplectic group.
arXiv Detail & Related papers (2024-03-07T15:43:17Z) - Polynomial-depth quantum algorithm for computing matrix determinant [46.13392585104221]
We propose an algorithm for calculating the determinant of a square matrix, and construct a quantum circuit realizing it.
Each row of the matrix is encoded as a pure state of some quantum system.
The admitted matrix is therefore arbitrary up to the normalization of quantum states of those systems.
arXiv Detail & Related papers (2024-01-29T23:23:27Z) - Infeasibility of constructing a special orthogonal matrix for the
deterministic remote preparation of arbitrary n-qubit state [2.3455770974978933]
We present a complex-complexity algorithm to construct a special orthogonal matrix for the remote deterministic state preparation (DRSP) of an arbitrary n-qubit state.
We use the proposed algorithm to confirm that the unique form does not have any solution when n>3, which means it is infeasible to construct such a special orthogonal matrix for the DRSP of an arbitrary n-qubit state.
arXiv Detail & Related papers (2023-09-23T11:06:34Z) - Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation [64.49871502193477]
We propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix.
Comprehensive experimental results on six commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods.
arXiv Detail & Related papers (2022-05-21T01:47:17Z) - Quantum algorithms for matrix operations and linear systems of equations [65.62256987706128]
We propose quantum algorithms for matrix operations using the "Sender-Receiver" model.
These quantum protocols can be used as subroutines in other quantum schemes.
arXiv Detail & Related papers (2022-02-10T08:12:20Z) - Matrix Decomposition and Applications [8.034728173797953]
In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition.
matrix decomposition has become a core technology in machine learning, largely due to the development of the back propagation algorithm in fitting a neural network.
arXiv Detail & Related papers (2022-01-01T08:13:48Z) - Non-PSD Matrix Sketching with Applications to Regression and
Optimization [56.730993511802865]
We present dimensionality reduction methods for non-PSD and square-roots" matrices.
We show how these techniques can be used for multiple downstream tasks.
arXiv Detail & Related papers (2021-06-16T04:07:48Z) - Periodicity of lively quantum walks on cycles with generalized Grover
coin [0.17205106391379021]
We extend the study of three state lively quantum walks on cycles by considering the coin operator as a linear sum of permutation matrices.
We establish that an orthogonal matrix of order $3times 3$ is a linear sum of permutation matrices if and only if it is permutative.
arXiv Detail & Related papers (2020-03-29T06:32:21Z) - Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion [101.83262280224729]
We develop a relative error bound for nuclear norm regularized matrix completion.
We derive a relative upper bound for recovering the best low-rank approximation of the unknown matrix.
arXiv Detail & Related papers (2015-04-26T13:12:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.