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20082024
most citedQuantum-Classical Computation of Schwinger Model Dynamics using Quantum Computers

439 citations · 3.7k across the 81 of their papers we have counts for

collaborators
Showing 2022Show all

8 papers · 1 filter

quant-ph2022★ 11 cited

Digital-analog co-design of the Harrow-Hassidim-Lloyd algorithm

Ana Martin, Ruben Ibarrondo, Mikel Sanz

The Harrow-Hassidim-Lloyd quantum algorithm was proposed to solve linear systems of equations and it is the core of various applications. However, there is not…

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★ 9 cited

Quantum vs classical genetic algorithms: A numerical comparison shows faster convergence

Rubén Ibarrondo, Giancarlo Gatti, Mikel Sanz

Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution. Quantum computation is a new computational paradigm which exploits quantum resources to sp…

quant-ph2022★ 38 cited

Propagating Quantum Microwaves: Towards Applications in Communication and Sensing

Mateo Casariego, Emmanuel Zambrini Cruzeiro, Stefano Gherardini +22

The field of propagating quantum microwaves has started to receive considerable attention in the past few years. Motivated at first by the lack of an efficient microwave-to-optical…

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…

quant-ph2022★ 26 cited

Quantum Genetic Algorithm with Individuals in Multiple Registers

Rubén Ibarrondo, Giancarlo Gatti, Mikel Sanz

Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution, which are characterized by successfully finding robust solutions for optimization problems…