collaborators

11 papers

quant-ph2026

Tensor-Network Formulation of the Traveling Salesman Problem and Variants

Alejandro Mata Ali, Iñigo Perez Delgado, Aitor Moreno Fdez. de Leceta

This work presents a tensor-network formulation of the Traveling Salesman Problem (TSP) and several of its variants. The approach represents candidate tours with tensor-network lay…

cs.LG2026

Anomaly Detection from a Tensor Train Perspective

Alejandro Mata Ali, Aitor Moreno Fdez. de Leceta, Jorge López Rubio

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 pres…

quant-ph2026

Quantum-inspired Techniques in Tensor Networks for Industrial Contexts

Alejandro Mata Ali, Iñigo Perez Delgado, Aitor Moreno Fdez. de Leceta

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, wit…

quant-ph2026

Solving Systems of Linear Equations: HHL from a Tensor Networks Perspective

Alejandro Mata Ali, Iñigo Perez Delgado, Marina Ristol Roura +2

This work presents a new approach for simulating the HHL linear systems of equations solver algorithm with tensor networks. First, a novel HHL in the qudits formalism, the generali…

cs.LG2026

Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms

Alejandro Mata Ali, Iñigo Perez Delgado, Marina Ristol Roura +1

We present two algorithms to initialize layers of tensorized neural networks and general tensor network algorithms using partial computations of their Frobenius norms and positive…

quant-ph2026

Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective

Alejandro Mata Ali, Iñigo Perez Delgado, Beatriz García Markaida +1

This work presents a novel method for task optimization in industrial plants using quantum-inspired tensor network technology. This method obtains the best possible combination of…