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publications
Optimization of image acquisition for earth observation satellites via quantum computing Permalink
Antón Makarov, Márcio M Taddei, Eneko Osaba, Giacomo Franceschetto, Esther Villar-Rodríguez, Izaskun Oregi – IDEAL 2023, 2023
Satellite image acquisition scheduling selects the optimal subset of images during an orbit pass under constraints. Although widely studied in AI and operations research, it has rarely been approached with quantum computing. We propose two QUBO formulations to handle the problem and test them on D-Wave quantum annealers and hybrid solvers across 20 benchmark instances.
Quantum optimization methods for satellite mission planning Permalink
Antón Makarov, Carlos Pérez-Herradón, Giacomo Franceschetto, Márcio M Taddei, Eneko Osaba, Paloma del Barrio Cabello, Esther Villar-Rodriguez, Izaskun Oregi – IEEE Access, 2024
We study satellite mission planning as a combinatorial optimization problem and develop methods to encode complex constraints for quantum computers. We experimentally evaluate quantum annealing and QAOA on realistic datasets, analyzing how problem structure impacts performance and establishing a baseline for current quantum optimization capabilities.
Demonstration of Quantum Projective Simulation on a Single-Photon-Based Quantum Computer Permalink
Giacomo Franceschetto, Arno Ricou – Physical Review A, 2024
We implement a projective-simulation-based variational reinforcement learning algorithm on Quandela’s single-photon quantum computer. Using quantum walks of photons across tunable beamsplitters and phase shifters, we solve a benchmark task and demonstrate the potential of a quantum agent over a classical one.
Harnessing quantum back-action for time-series processing Permalink
Giacomo Franceschetto, Marcin Płodzień, Maciej Lewenstein, Antonio Acín, Pere Mujal – arXiv, 2024
We show that optimizing indirect measurements in quantum reservoir computing improves execution time and overall performance. By tuning both the reservoir Hamiltonian and measurement strength across benchmarking tasks, we provide a practical approach for enhancing indirect measurement-based protocols.
Hamiltonian learning via quantum Zeno effect Permalink
Giacomo Franceschetto, Egle Pagliaro, Luciano Pereira, Leonardo Zambrano, Antonio Acín – arXiv, 2025
We propose a Hamiltonian learning protocol that leverages the quantum Zeno effect to reshape and localize system dynamics, enabling the extraction of local Hamiltonian coefficients, and demonstrate its feasibility by learning a 109-qubit Hamiltonian on IBM’s hardware.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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