Research

I’m broadly interested in algorithms on and for quantum devices, with a focus on approaches that are experimentally friendly. My work includes developing algorithms to verify and benchmark quantum simulators, with the goal of creating methods that are both practical and scalable for real-world devices. In parallel, I study machine learning models implemented with quantum systems, where I have worked on the demonstration and interpretability of quantum projective simulation in single-photon devices and on the design of quantum reservoir computing protocols. Earlier, during my Master’s, I also explored quantum optimization, focusing on how to encode higher-order optimization problems in the QUBO formalism.

Preprints

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.

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.

Published

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.

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.

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.