Weakly Hadamard diagonalizable graphs and Quantum State Transfer
- URL: http://arxiv.org/abs/2307.01859v2
- Date: Wed, 10 Jul 2024 17:27:03 GMT
- Title: Weakly Hadamard diagonalizable graphs and Quantum State Transfer
- Authors: Darian McLaren, Hermie Monterde, Sarah Plosker,
- Abstract summary: We study Hadamard diagonalizable graphs in the context of quantum state transfer.
We provide numerous properties and constructions of weak Hadamard matrices and weakly Hadamard diagonalizable graphs.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hadamard diagonalizable graphs are undirected graphs for which the corresponding Laplacian is diagonalizable by a Hadamard matrix. Such graphs have been studied in the context of quantum state transfer. Recently, the concept of a weak Hadamard matrix was introduced: a $\{-1,0, 1\}$-matrix $P$ such that $PP^T$ is tridiagonal, as well as the concept of weakly Hadamard diagonalizable graphs. We therefore naturally explore quantum state transfer in these generalized Hadamards. Given the infancy of the topic, we provide numerous properties and constructions of weak Hadamard matrices and weakly Hadamard diagonalizable graphs in order to better understand them.
Related papers
- The exact quantum chromatic number of Hadamard graphs [0.0]
We compute the quantum chromatic numbers of Hadamard graphs of order $n=2N$ for $N$ a multiple of $4$.
We also compute the exact quantum chromatic number of the categorical product of Hadamard graphs.
arXiv Detail & Related papers (2024-09-27T14:28:57Z) - Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping [56.57574396804837]
Clustering is a fundamental task in machine learning and data science, and similarity graph-based clustering is an important approach within this domain.
We study the relationship between the Marcus mapping and optimal transport.
We prove that the Marcus mapping solves a specific type of optimal transport problem and demonstrate that solving this problem through Marcus mapping is more efficient than directly applying optimal transport methods.
arXiv Detail & Related papers (2024-08-06T03:34:43Z) - Efficient conversion from fermionic Gaussian states to matrix product states [48.225436651971805]
We propose a highly efficient algorithm that converts fermionic Gaussian states to matrix product states.
It can be formulated for finite-size systems without translation invariance, but becomes particularly appealing when applied to infinite systems.
The potential of our method is demonstrated by numerical calculations in two chiral spin liquids.
arXiv Detail & Related papers (2024-08-02T10:15:26Z) - A generalization of quantum pair state transfer [0.0]
An $s$-pair state in a graph is a quantum state of the form $mathbfe_u+smathbfe_v$.
We develop the theory of perfect $s$-pair state transfer in continuous quantum walks.
arXiv Detail & Related papers (2024-04-25T14:45:49Z) - One-sided Matrix Completion from Two Observations Per Row [95.87811229292056]
We propose a natural algorithm that involves imputing the missing values of the matrix $XTX$.
We evaluate our algorithm on one-sided recovery of synthetic data and low-coverage genome sequencing.
arXiv Detail & Related papers (2023-06-06T22:35:16Z) - Quantum Information Masking of Hadamard Sets [0.0]
We study quantum information masking of arbitrary dimensional states.
We define the so called Hadamard set of quantum states whose Gram-Schmidt matrix can be diagonalized by Hadamard unitary matrices.
arXiv Detail & Related papers (2021-09-30T02:54:45Z) - Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov
Random Fields [51.07460861448716]
This paper presents a convex-analytic framework to learn from data.
We show that a triangular convexity decomposition is guaranteed by a transform of the corresponding to its upper part.
arXiv Detail & Related papers (2021-09-17T17:46:12Z) - Symplectic decomposition from submatrix determinants [0.0]
An important theorem in Gaussian quantum information tells us that we can diagonalise the covariance matrix of any Gaussian state via a symplectic transformation.
Inspired by a recently presented technique for finding the eigenvectors of a Hermitian matrix from certain submatrix eigenvalues, we derive a similar method for finding the diagonalising symplectic from certain submatrix determinants.
arXiv Detail & Related papers (2021-08-11T18:00:03Z) - 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) - Optimal Iterative Sketching with the Subsampled Randomized Hadamard
Transform [64.90148466525754]
We study the performance of iterative sketching for least-squares problems.
We show that the convergence rate for Haar and randomized Hadamard matrices are identical, andally improve upon random projections.
These techniques may be applied to other algorithms that employ randomized dimension reduction.
arXiv Detail & Related papers (2020-02-03T16:17:50Z) - Complex Hadamard Diagonalisable Graphs [0.0]
We show that a large class of complex Hadamard diagonalisable graphs have sets forming an equitable partition.
We provide examples and constructions of complex Hadamard diagonalisable graphs.
We discuss necessary and sufficient conditions for $(alpha, beta)$--Laplacian fractional revival and perfect state transfer.
arXiv Detail & Related papers (2020-01-01T17:49:25Z)
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.