Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Aitor, Moreno"'
We present a series of algorithms in tensor networks for anomaly detection in datasets, by using data compression in a Tensor Train representation. These algorithms consist of preserving the structure of normal data in compression and deleting the st
Externí odkaz:
http://arxiv.org/abs/2409.15030
In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use
Externí odkaz:
http://arxiv.org/abs/2404.11277
In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use
Externí odkaz:
http://arxiv.org/abs/2404.17645
We present a novel quantum-inspired algorithm for solving the Traveling Salesman Problem (TSP) and some of its variations using tensor networks. This approach consists on the simulated initialization of a quantum system with superposition of all poss
Externí odkaz:
http://arxiv.org/abs/2311.14344
Autor:
Ali, Alejandro Mata, Delgado, Iñigo Perez, Markaida, Beatriz García, de Leceta, Aitor Moreno Fdez.
We present a novel method for task optimization in industrial plants using quantum-inspired tensor network technology. This method allows us to obtain the best possible combination of tasks on a set of machines with a set of constraints without havin
Externí odkaz:
http://arxiv.org/abs/2311.10433
Autor:
Delgado, Iñigo Perez, Markaida, Beatriz García, de Leceta, Aitor Moreno Fdez., Uriarte, Jon Ander Ochoa
Publikováno v:
2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022, pp. 923-929
In this paper, we present an implementation of a Job Selection Problem (JSP) -- a generalization of the well-known Travelling Salesperson Problem (TSP) -- of $N=9$ jobs on its Quadratic Unconstrained Binary Optimization (QUBO) form, using $\mathcal{O
Externí odkaz:
http://arxiv.org/abs/2309.16522
Autor:
Delgado, Iñigo Perez, Markaida, Beatriz García, Ali, Alejandro Mata, de Leceta, Aitor Moreno Fdez.
Publikováno v:
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
We present a subproblemation scheme for heuristical solving of the JSP (Job Reassignment Problem). The cost function of the JSP is described via a QUBO hamiltonian to allow implementation in both gate-based and annealing quantum computers. For a job
Externí odkaz:
http://arxiv.org/abs/2309.16473
Autor:
Ali, Alejandro Mata, Delgado, Iñigo Perez, Roura, Marina Ristol, de Leceta, Aitor Moreno Fdez.
We present an algorithm for solving tridiagonal Quadratic Unconstrained Binary Optimization (QUBO) problems and Quadratic Unconstrained Discrete Optimization (QUDO) problems with one-neighbor interactions using the quantum-inspired technology of tens
Externí odkaz:
http://arxiv.org/abs/2309.10509
Autor:
Ali, Alejandro Mata, Delgado, Iñigo Perez, Roura, Marina Ristol, de Leceta, Aitor Moreno Fdez., Romero, Sebastián V.
We present an algorithm for solving systems of linear equations based on the HHL algorithm with a novel qudits methodology, a generalization of the qubits with more states, to reduce the number of gates to be applied and the amount of resources. Base
Externí odkaz:
http://arxiv.org/abs/2309.05290
Autor:
Ali, Alejandro Mata, Delgado, Iñigo Perez, Roura, Marina Ristol, de Leceta, Aitor Moreno Fdez.
We present a novel method for initializing layers of tensorized neural networks in a way that avoids the explosion of the parameters of the matrix it emulates. The method is intended for layers with a high number of nodes in which there is a connecti
Externí odkaz:
http://arxiv.org/abs/2309.06577