Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Numerical calculation of the measure of entanglement of qutrits multiparty

Werther Xisto da Silva Cardoso

Published Jan 1, 2011• 0 citations
OpenAlex

Abstract

This work discusses the numerical calculation of the measure of entanglement by minimizing the Shannon entropy. This is an optimization problem and it becomes hard when the number of quantum levels of the system and the number of parts increase. A genetic algorithm is presented for calculating the measure of entanglement of qutrits.

Related papers

Entanglement measure for multipartite pure states and its numerical calculation

Andrey Yu. Chernyavskiy

The quantification and classification of quantum entanglement is a very important and still open question of quantum information theory. In this paper, we describe an entanglement measure for multipartite pure states (the minimum of Shannon's entropy of orthogonal measurements). This measure is additive, monotone under LOCC, and coincides with the reduced von Neumann entropy on bipartite states. A method for numerical calculation of this measure by genetic algorithms is also presented. Moreover, the minimization of entropy technique is extended to fermionic states.

arXiv:0905.0201•May 2, 2009•2 citations

Calculation of Quantum Entanglement

Jingshui Yu, Wenbo Xu

Quantum entanglement which is used as the basis for such applications as quantum cryptography, quantum teleportation, and quantum computing, is a multifaceted property that has attracted much attention. The calculation of quantum entanglement therefore has become important. But the calculation of entanglement has become much more complex as systems that use entanglement have evolved. Based on the relative entropy of entanglement, a general method was developed for the calculation of entanglement for n-qubit or n-qudit system, which using an optimization algorithm technique such as genetic algorithm or Quantum-behaved Particle Swarm Optimization. The method using different optimization algorithm was tested on a two qubit system, for which there are some known points, and compared to exact calculations using the entanglement of formation and with each other. Advantages and disadvantages of the method are discussed and directions of future research are exposed.

International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)•Oct 1, 2011•0 citations

Evolution Of Entanglement In Groverian Search Algorithm: Two-Qutrit System

Arti Chamoli, Chandra Mohan Bhandari

Entanglement plays a crucial role in quantum processes particularly those pertaining to quantum information and computation. An analytical expression for entanglement measure defined in terms of success rate of Grover's search algorithm has been obtained for a two-qutrit system and the calculated results agree well with the conventional entropy based measure. Qutrit system are of special interest because the Hilbert space dimensionality (for a given number of qudits-d-dimensional system)is optimal for d=3 and this may be of significance in the enhancement of computing power.

arXiv:quant-ph/0609010•Sep 2, 2006•0 citations

A numerical solution for the accessible quantum information problem

Edmar Nascimento, Francisco de Assis

The quantum discrimination problem is an important topic of quantum information theory. Discriminating among a set of M non-orthogonal quantum states means choosing among one of them based on a quantum measurement. In order to accomplish this task one must choose a measure of information to rank the potential solutions. When the Shannon mutual information is considered as such a measure one has an accessible information problem. The accessible information problem consists on the maximization of the mutual information over all sorts of quantum measurements. Analytical solutions for this optimization problem are not available for the general case. Even numerical solutions are hard to find because of the nonlinear nature of the mutual information function. On the other hand, genetic algorithms work pretty well with "misbehaved" functions. Based on the previous statements, we propose solving the accessible information problem via genetic algorithms.

International Telecommunications Symposium (ITS)•Sep 1, 2006•1 citation

Evolution of Entanglement in Groverian Search Algorithm: n-qudit System

Arti Chamoli, Chandra Mohan Bhandari

Entanglement plays a crucial role in quantum processes particularly those pertaining to quantum information and computation. An analytical expression for an entanglement measure defined in terms of success rate of Grover's search algorithm has been obtained for qutrit systems with real coefficients and the calculated results agree well with the conventional entropy based measure. The entanglement measure is further generalised for qudit (d-level) systems with real coefficients.

arXiv:quant-ph/0702222•Feb 24, 2007•1 citation

Calculation of quantum discord and entanglement measures using the random mutations optimization algorithm

Andrey Yu. Chernyavskiy

The simple and universal global optimization method based on simplified multipopulation genetic algorithm is presented. The method is applied to quantum information problems. It is compared to the genetic algorithm on standard test functions, and also tested on the calculation of quantum discord and minimal entanglement entropy, which is an entanglement measure for pure multipartite states.

arXiv:1304.3703•Apr 12, 2013•3 citations
The quantification and classification of quantum entanglement is a very important and still open question of quantum information theory. In this paper, we describe an entanglement measure for multipartite pure states (the minimum of Shannon's entropy of orthogonal measurements). This measure is additive, monotone under LOCC, and coincides with the reduced von Neumann entropy on bipartite states. A method for numerical calculation of this measure by genetic algorithms is also presented. Moreover, the minimization of entropy technique is extended to fermionic states.
Quantum entanglement which is used as the basis for such applications as quantum cryptography, quantum teleportation, and quantum computing, is a multifaceted property that has attracted much attention. The calculation of quantum entanglement therefore has become important. But the calculation of entanglement has become much more complex as systems that use entanglement have evolved. Based on the relative entropy of entanglement, a general method was developed for the calculation of entanglement for n-qubit or n-qudit system, which using an optimization algorithm technique such as genetic algorithm or Quantum-behaved Particle Swarm Optimization. The method using different optimization algorithm was tested on a two qubit system, for which there are some known points, and compared to exact calculations using the entanglement of formation and with each other. Advantages and disadvantages of the method are discussed and directions of future research are exposed.
Entanglement plays a crucial role in quantum processes particularly those pertaining to quantum information and computation. An analytical expression for entanglement measure defined in terms of success rate of Grover's search algorithm has been obtained for a two-qutrit system and the calculated results agree well with the conventional entropy based measure. Qutrit system are of special interest because the Hilbert space dimensionality (for a given number of qudits-d-dimensional system)is optimal for d=3 and this may be of significance in the enhancement of computing power.
The quantum discrimination problem is an important topic of quantum information theory. Discriminating among a set of M non-orthogonal quantum states means choosing among one of them based on a quantum measurement. In order to accomplish this task one must choose a measure of information to rank the potential solutions. When the Shannon mutual information is considered as such a measure one has an accessible information problem. The accessible information problem consists on the maximization of the mutual information over all sorts of quantum measurements. Analytical solutions for this optimization problem are not available for the general case. Even numerical solutions are hard to find because of the nonlinear nature of the mutual information function. On the other hand, genetic algorithms work pretty well with "misbehaved" functions. Based on the previous statements, we propose solving the accessible information problem via genetic algorithms.