A quantum compiler design method by using linear combinations of permutations
- URL: http://arxiv.org/abs/2404.18226v1
- Date: Sun, 28 Apr 2024 15:42:37 GMT
- Title: A quantum compiler design method by using linear combinations of permutations
- Authors: Ammar Daskin,
- Abstract summary: We describe a method to write a given generic matrix in terms of quantum gates based on the block encoding.
We first show how to convert a matrix into doubly matrices and by using Birkhoff's algorithm, we express that matrix in terms of a linear combination of permutations which can be mapped to quantum circuits.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A matrix can be converted into a doubly stochastic matrix by using two diagonal matrices. And a doubly stochastic matrix can be written as a sum of permutation matrices. In this paper, we describe a method to write a given generic matrix in terms of quantum gates based on the block encoding. In particular, we first show how to convert a matrix into doubly stochastic matrices and by using Birkhoff's algorithm, we express that matrix in terms of a linear combination of permutations which can be mapped to quantum circuits. We then discuss a few optimization techniques that can be applied in a possibly future quantum compiler software based on the method described here.
Related papers
- 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 tree-approach Pauli decomposition algorithm with application to quantum computing [0.0]
We propose an algorithm with a parallel implementation that optimize this decomposition using a tree approach.
We also explain how some particular matrix structures can be exploited to reduce the number of operations.
arXiv Detail & Related papers (2024-03-18T10:38:06Z) - Vectorization of the density matrix and quantum simulation of the von
Neumann equation of time-dependent Hamiltonians [65.268245109828]
We develop a general framework to linearize the von-Neumann equation rendering it in a suitable form for quantum simulations.
We show that one of these linearizations of the von-Neumann equation corresponds to the standard case in which the state vector becomes the column stacked elements of the density matrix.
A quantum algorithm to simulate the dynamics of the density matrix is proposed.
arXiv Detail & Related papers (2023-06-14T23:08:51Z) - A Quantum Algorithm for Functions of Multiple Commuting Hermitian
Matrices [0.0]
We introduce the quantum eigenvalue transformation for functions of commuting Hermitian.
We then present a framework for working with normal matrix functions in which we may solve MQET.
arXiv Detail & Related papers (2023-02-22T04:23:05Z) - A quantum algorithm for solving eigenproblem of the Laplacian matrix of
a fully connected weighted graph [4.045204834863644]
We propose an efficient quantum algorithm to solve the eigenproblem of the Laplacian matrix of a fully connected weighted graph.
Specifically, we adopt the optimal Hamiltonian simulation technique based on the block-encoding framework.
We also show that our algorithm can be extended to solve the eigenproblem of symmetric (non-symmetric) normalized Laplacian matrix.
arXiv Detail & Related papers (2022-03-28T02:24:08Z) - 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) - Fast Differentiable Matrix Square Root [65.67315418971688]
We propose two more efficient variants to compute the differentiable matrix square root.
For the forward propagation, one method is to use Matrix Taylor Polynomial (MTP)
The other method is to use Matrix Pad'e Approximants (MPA)
arXiv Detail & Related papers (2022-01-21T12:18:06Z) - 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) - Quantum Algorithms based on the Block-Encoding Framework for Matrix
Functions by Contour Integrals [1.5293427903448018]
We show a framework to implement the linear combination of the inverses on quantum computers.
We propose a quantum algorithm for matrix functions based on the framework.
arXiv Detail & Related papers (2021-06-15T12:10:35Z) - 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)
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.