activity
20202025
most citedA Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems

407 citations · 569 across the 30 of their papers we have counts for

collaborators

30 papers

quant-ph2025★ 1 cited

Exploring the application of quantum technologies to industrial and real-world use cases

Eneko Osaba, Esther Villar-Rodriguez, Izaskun Oregi

Recent advancements in quantum computing are leading to an era of practical utility, enabling the tackling of increasingly complex problems. The goal of this era is to leverage qua…

cs.ET2025

Optimizing Package Delivery with Quantum Annealers: Addressing Time-Windows and Simultaneous Pickup and Delivery

Eneko Osaba, Esther Villar-Rodriguez, Pablo Miranda-Rodriguez +1

Recent research at the intersection of quantum computing and routing problems has been highly prolific. Much of this work focuses on classical problems such as the Traveling Salesm…

quant-ph2025

Addressing the Minor-Embedding Problem in Quantum Annealing and Evaluating State-of-the-Art Algorithm Performance

Aitor Gomez-Tejedor, Eneko Osaba, Esther Villar-Rodriguez

This study addresses the minor-embedding problem, which involves mapping the variables of an Ising model onto a quantum annealing processor. The primary motivation stems from the o…

quant-ph2025

Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share

Eneko Osaba, Esther Villar-Rodriguez

Being immersed in the NISQ-era, current quantum annealers present limitations for solving optimization problems efficiently. To mitigate these limitations, D-Wave Systems developed…

quant-ph2025★ 1 cited

On the Transfer of Knowledge in Quantum Algorithms

Esther Villar-Rodriguez, Eneko Osaba, Izaskun Oregi +2

Quantum computing is poised to transform computational paradigms across science and industry. As the field evolves, it can benefit from established classical methodologies, includi…

cs.NE2024★ 407 cited

A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems

Eneko Osaba, Esther Villar-Rodriguez, Javier Del Ser +6

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research st…

cs.ET2024

Exploring Utility in a Real-World Warehouse Optimization Problem: Formulation Based on Quantum Annealers and Preliminary Results

Eneko Osaba, Esther Villar-Rodriguez, Antón Asla

In the current NISQ-era, one of the major challenges faced by researchers and practitioners lies in figuring out how to combine quantum and classical computing in the most efficien…

quant-ph2024★ 12 cited

QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems

Eneko Osaba, Esther Villar-Rodriguez

In this paper, we propose a quantum computing oriented benchmark for combinatorial optimization. This benchmark, coined as QOPTLib, is composed of 40 instances equally distributed…

quant-ph2024★ 17 cited

Quantum Optimization Methods for Satellite Mission Planning

Antón Makarov, Carlos Pérez-Herradón, Giacomo Franceschetto +5

Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to co…

cs.ET2024

Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers

Eneko Osaba, Esther Villar-Rodriguez, Antón Asla

Research focused on the conjunction between quantum computing and routing problems has been very prolific in recent years. Most of the works revolve around classical problems such…

cs.ET2024

Hybrid Quantum Solvers in Production: how to succeed in the NISQ era?

Eneko Osaba, Esther Villar-Rodriguez, Aitor Gomez-Tejedor +1

Hybrid quantum computing is considered the present and the future within the field of quantum computing. Far from being a passing fad, this trend cannot be considered just a stopga…

cs.AI2023

A Quantum Computing-based System for Portfolio Optimization using Future Asset Values and Automatic Reduction of the Investment Universe

Eneko Osaba, Guillaume Gelabert, Esther Villar-Rodriguez +2

One of the problems in quantitative finance that has received the most attention is the portfolio optimization problem. Regarding its solving, this problem has been approached usin…

quant-ph2023

Hybrid classical-quantum computing: are we forgetting the classical part in the binomial?

Esther Villar-Rodriguez, Aitor Gomez-Tejedor, Eneko Osaba

The expectations arising from the latest achievements in the quantum computing field are causing that researchers coming from classical artificial intelligence to be fascinated by…

cs.AI2023★ 9 cited

Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach

Sebastián V. Romero, Eneko Osaba, Esther Villar-Rodriguez +1

The Bin Packing Problem is a classic problem with wide industrial applicability. In fact, the efficient packing of items into bins is one of the toughest challenges in many logisti…

quant-ph2023★ 8 cited

Optimization of Image Acquisition for Earth Observation Satellites via Quantum Computing

Antón Makarov, Márcio M. Taddei, Eneko Osaba +3

Satellite image acquisition scheduling is a problem that is omnipresent in the earth observation field; its goal is to find the optimal subset of images to be taken during a given…

cs.AI2023★ 11 cited

Benchmark dataset and instance generator for Real-World Three-Dimensional Bin Packing Problems

Eneko Osaba, Esther Villar-Rodriguez, Sebastián V. Romero

In this article, a benchmark for real-world bin packing problems is proposed. This dataset consists of 12 instances of varying levels of complexity regarding size (with the number…

cs.LG2023★ 7 cited

Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments

Alain Andres, Lukas Schäfer, Stefano V. Albrecht +1

One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large…

cs.ET2023★ 36 cited

Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers

Sebastián V. Romero, Eneko Osaba, Esther Villar-Rodriguez +2

Efficient packing of items into bins is a common daily task. Known as Bin Packing Problem, it has been intensively studied in the field of artificial intelligence, thanks to the wi…

cs.LG2022

Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation

Alain Andres, Esther Villar-Rodriguez, Javier Del Ser

Reinforcement Learning has emerged as a strong alternative to solve optimization tasks efficiently. The use of these algorithms highly depends on the feedback signals provided by t…

quant-ph2022★ 5 cited

Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States

Ruilin Liu, Sebastián V. Romero, Izaskun Oregi +3

Coherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning,and quantum optics. In this article, two ways…

quant-ph2022★ 13 cited

Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem

Mikel Garcia-de-Andoin, Izaskun Oregi, Esther Villar-Rodriguez +2

The Bin Packing Problem (BPP) stands out as a paradigmatic combinatorial optimization problem in logistics. Quantum and hybrid quantum-classical algorithms are expected to show an…

quant-ph2022★ 1 cited

Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations

Esther Villar-Rodriguez, Eneko Osaba, Izaskun Oregi

Despite being considered as the next frontier in computation, Quantum Computing is still in an early stage of development. Indeed, current commercial quantum computers suffer from…

cs.LG2022★ 6 cited

An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments

Alain Andres, Esther Villar-Rodriguez, Javier Del Ser

In the last few years, the research activity around reinforcement learning tasks formulated over environments with sparse rewards has been especially notable. Among the numerous ap…

quant-ph2022★ 16 cited

Hybrid Quantum-Classical Heuristic for the Bin Packing Problem

Mikel Garcia de Andoin, Eneko Osaba, Izaskun Oregi +2

Optimization problems is one of the most challenging applications of quantum computers, as well as one of the most relevants. As a consequence, it has attracted huge efforts to obt…