Search Results for author: Ivano Tavernelli

Found 11 papers, 2 papers with code

Symmetry-invariant quantum machine learning force fields

no code implementations19 Nov 2023 Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, Francesco Tacchino

Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning, and that chemical systems represent, in fact, an interesting and rich playground for the development and application of advanced quantum machine learning tools.

Atomic Forces Quantum Machine Learning

Unravelling physics beyond the standard model with classical and quantum anomaly detection

no code implementations25 Jan 2023 Julian Schuhmacher, Laura Boggia, Vasilis Belis, Ema Puljak, Michele Grossi, Maurizio Pierini, Sofia Vallecorsa, Francesco Tacchino, Panagiotis Barkoutsos, Ivano Tavernelli

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC).

Anomaly Detection

Quantum anomaly detection in the latent space of proton collision events at the LHC

1 code implementation25 Jan 2023 Kinga Anna Woźniak, Vasilis Belis, Ema Puljak, Panagiotis Barkoutsos, Günther Dissertori, Michele Grossi, Maurizio Pierini, Florentin Reiter, Ivano Tavernelli, Sofia Vallecorsa

The designed quantum anomaly detection models, namely an unsupervised kernel machine and two clustering algorithms, are trained to find new-physics events in the latent representation of LHC data produced by the autoencoder.

Anomaly Detection Quantum Machine Learning

Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage

no code implementations8 Apr 2022 Stefano Mensa, Emre Sahin, Francesco Tacchino, Panagiotis Kl. Barkoutsos, Ivano Tavernelli

Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19.

Drug Discovery Quantum Machine Learning

Quantum neural networks force fields generation

no code implementations9 Mar 2022 Oriel Kiss, Francesco Tacchino, Sofia Vallecorsa, Ivano Tavernelli

Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales.

BIG-bench Machine Learning Quantum Machine Learning

Variational learning for quantum artificial neural networks

no code implementations3 Mar 2021 Francesco Tacchino, Stefano Mangini, Panagiotis Kl. Barkoutsos, Chiara Macchiavello, Dario Gerace, Ivano Tavernelli, Daniele Bajoni

In the last few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed.

Quantum Physics

Deterministic Algorithms for Compiling Quantum Circuits with Recurrent Patterns

no code implementations17 Feb 2021 Davide Ferrari, Ivano Tavernelli, Michele Amoretti

In particular, such patterns appear in quantum circuits that are used to compute the ground state properties of molecular systems using the variational quantum eigensolver (VQE) method together with the RyRz heuristic wavefunction Ansatz.

Quantum Physics Computational Complexity Data Structures and Algorithms

Non-unitary operations for ground-state calculations in near term quantum computers

no code implementations4 Oct 2019 Guglielmo Mazzola, Pauline Ollitrault, Panagiotis Barkoutsos, Ivano Tavernelli

We introduce a quantum Monte Carlo inspired reweighting scheme to accurately compute energies from optimally short quantum circuits.

Quantum Physics Statistical Mechanics Computational Physics

Variational Quantum Simulation of Ultrastrong Light-Matter Coupling

no code implementations18 Sep 2019 Agustin Di Paolo, Panagiotis Kl. Barkoutsos, Ivano Tavernelli, Alexandre Blais

We propose the simulation of quantum-optical systems in the ultrastrong-coupling regime using a variational quantum algorithm.

Quantum Physics

Improving Variational Quantum Optimization using CVaR

2 code implementations10 Jul 2019 Panagiotis Kl. Barkoutsos, Giacomo Nannicini, Anton Robert, Ivano Tavernelli, Stefan Woerner

The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically.

Quantum Physics

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