Research

My research lies at the intersection of quantum information, quantum learning, and many-body dynamics, with a focus on methods that are both theoretically well-founded and experimentally accessible.
A major part of my work concerns learning and characterizing the dynamics of quantum systems, including scalable approaches to Hamiltonian and Lindbladian learning that exploit locality and structure.
In parallel, I investigate how quantum systems can themselves be used for learning and information processing, particularly through quantum reservoir computing and quantum projective simulation, with an emphasis on temporal processing, measurement-based protocols, and experimentally realistic implementations.

More broadly, I am interested in developing quantum algorithms and learning protocols that make effective use of the native dynamics and capabilities of quantum devices.
Earlier, during my Master’s, I also worked on quantum optimization, focusing on the encoding of higher-order optimization problems in the QUBO formalism.

Preprints

Rigorous Time-dependent Hamiltonian Learning via Continuous Weak Measurements Permalink

Jesús Jiménez-Rodríguez, Giacomo Franceschetto, Antonio Acín, Luciano Pereira – arXiv, 2026

We develop an experimentally friendly protocol for learning time-dependent many-body Hamiltonians from continuous weak measurements. By exploiting interaction sparsity, the global reconstruction reduces to local inverse problems, for which we derive explicit reconstruction-error and sample-complexity guarantees.

Measurement-enabled online quantum processing with amplitude encoding Permalink

Giacomo Franceschetto, Pere Mujal, Rodrigo Martínez-Peña – arXiv, 2026

We introduce an online quantum reservoir computing protocol that implements amplitude encoding using mid-circuit measurement and reset, while indirect measurements provide access to reservoir observables without interrupting temporal processing. We demonstrate the protocol in a proof-of-principle implementation on quantum hardware.

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.

Published

Harnessing quantum back-action for time-series processing Permalink

Giacomo Franceschetto, Marcin Płodzień, Maciej Lewenstein, Antonio Acín, Pere Mujal – Physical Review X, 2026

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 to enhance indirect measurement-based protocols.

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.