Conveners
Quantum Machine Learning
- Stefano Giagu (Sapienza Università di Roma and Istituto Nazionale di Fisica Nucleare)
Quantum Machine Learning
- Stefano Giagu (Sapienza Università di Roma and Istituto Nazionale di Fisica Nucleare)
Quantum Machine Learning
- Andrea Giachero (INFN Milano-Bicocca)
Quantum Machine Learning
- Andrea Giachero (INFN Milano-Bicocca)
Description
Algorithms and hybrid ML/quantum computing approaches, with applications to high-energy physics and data analysis in general.
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Dr Michele Grossi (CERN)04/02/2026, 09:00
Quantum computing provides a natural framework for generative modeling through sampling tasks with established complexity-theoretic advantages, yet standard parametrized-circuit approaches face persistent challenges in trainability and scalability. This talk reports recent progress on a differentiable quantum generative model (DQGM) based on quantum Chebyshev transforms, which enables...
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Federico Andrea Guillaume Corchia (University of Bologna and INFN Bologna)04/02/2026, 09:20
The increasing demands on simulation statistics for HL-LHC analyses challenge the scalability of traditional calorimeter simulation within all LHC collaborations. Fast simulation techniques based on machine learning have proven effective, yet further improvements may arise from quantum-inspired models.
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We investigate the feasibility of integrating Quantum Neural Network (QNN) components into... -
Lorenzo Sestini (Istituto Nazionale di Fisica Nucleare)04/02/2026, 09:40
In this talk, an overview of quantum machine learning (QML) activities developed within the LHCb experiment will be presented, with particular emphasis on their applications to classification and simulation tasks. Examples include the jet flavour identification as well as quantum generative models aimed at accelerating the detector simulations. It will be shown how quantum algorithms can...
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Eric Ballabene (Istituto Nazionale di Fisica Nucleare)04/02/2026, 10:00
This talk investigates the application of Quantum Machine Learning (QML) simulated algorithms to classification problems in High Energy Physics, where distinguishing signal events from complex background processes is essential. We focus on the Higgs boson decay channel 𝐻→𝑍𝑍*→4ℓ, a clean yet statistically limited signature. The study evaluates the performance of QML models and examines their...
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Fabrizio Napolitano (Istituto Nazionale di Fisica Nucleare)04/02/2026, 10:20
The study of Vector Boson Scattering (VBS) at LHC provides a unique window into the electroweak symmetry breaking mechanism. The polarization of the vector bosons enables precision tests of the heart Standard Model at the TeV scale, additionally sensibile to new physics. The highest experimental sensitivity can be achieved in the boosted regime, where the bosons are produced with large...
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Alberto Coppi (Istituto Nazionale di Fisica Nucleare)04/02/2026, 11:15
Tensor Networks (TN), originally developed in the context of quantum many-body physics, have recently emerged as powerful and interpretable Machine Learning (ML) architectures. The objective of this talk is twofold. Firstly, provide an intuitive and practical perspective on TNML methods, starting from the seminal work that initially established its connection to supervised learning. Secondly,...
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Matteo Argenton (Istituto Nazionale di Fisica Nucleare)04/02/2026, 11:35
Identity management (IM) for multi-object tracking is the problem of evolving a belief state over track–object associations, accounting for mixing events and measurement uncertainties.
In the most general setting the problem state is described by a probability distribution over the $n!$ permutations of $n$ objects, whose exact representation and update are inefficient in classical...
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Concezio Bozzi (Istituto Nazionale di Fisica Nucleare)04/02/2026, 11:55
The LHC detectors are going to be upgraded to profit from the substantial increase of the LHC instantaneous luminosity. In particular, in the forthcoming High Luminosity phase of ATLAS and CMS the number of proton–proton interactions per beam crossing is expected to rise by a factor three, averaging 140–200 in future Runs 4 and 5. Similar upgrades are also planned for the LHCb and ALICE...
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Lorenzo Borella (Istituto Nazionale di Fisica Nucleare)04/02/2026, 12:15
Tensor Networks (TNs) are a powerful computational framework originally developed for the efficient representation and simulation of quantum many-body systems. In recent years, they have gained increasing attention in machine learning (ML), demonstrating competitive performance in supervised learning tasks compared to conventional models.
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In this work, we investigate the suitability of Tree... -
Dr Deborah Volpe (Istituto Nazionale di Geofisica e Vulcanologia)04/02/2026, 12:35
Quantum computers promise to revolutionize computation. However, nowadays, their usefulness is still strongly limited by hardware noise, limited device availability, and the considerable cost of simulating quantum circuits on classical machines. These challenges become particularly evident when considering real-world applications, such as training Quantum Neural Network (QNNs), where the...
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Leonardo Banchi (Istituto Nazionale di Fisica Nucleare)05/02/2026, 09:00
Gradient-based optimization is a key ingredient of variational quantum algorithms, with applications ranging from quantum machine learning to quantum chemistry and simulation. The parameter-shift rule provides a hardware-friendly method for evaluating gradients of expectation values with respect to circuit parameters, but its applicability is limited to circuits whose gate generators have a...
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Simone Bordoni (Sapienza universita di Roma)05/02/2026, 09:25
This work presents a novel machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. By leveraging reinforcement learning, we train an agent to introduce noise channels that accurately mimic specific noise patterns. The proposed noise characterization method has been tested on simulations for small quantum circuits, where it con- sistently...
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María Laura Olivera Atencio (Universidad de Sevilla)05/02/2026, 09:50
We introduce a reinforcement learning algorithm designed to identify the fixed points of a given quantum operation. The method iteratively constructs the unitary transformation that maps the computational basis onto the basis of fixed points through a reward-penalty scheme based on quantum measurements. In cases where the operation corresponds to a Hamiltonian evolution, this task reduces to...
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Prof. Jesús Casado Pascual (Universidad de Sevilla)05/02/2026, 10:15
A study of the effect of thermal dissipation on quantum reinforcement learning is performed. For this purpose, a nondissipative quantum reinforcement learning protocol is adapted to the presence of thermal dissipation. Analytical calculations as well as numerical simulation are carried out, obtaining evidence that dissipation does not significantly degrade the performance of the quantum...
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Andrea Cacioppo (Istituto Nazionale di Fisica Nucleare)06/02/2026, 09:00
Financial fraud detection is a highly imbalanced and dynamic learning problem that challenges traditional machine learning models. Quantum machine learning (QML) offers new representational paradigms capable of exploring high-dimensional feature spaces in ways substantially different from classical systems, leveraging quantum superposition and entanglement. In this study, we present a...
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Laura Cappelli (Istituto Nazionale di Fisica Nucleare)06/02/2026, 09:20
In the field of finance, lenders must estimate the creditworthiness of loan applicants, which leads them to devise strategies for classifying borrowers. Among these strategies, banking groups identify the definition of a rating scale as a classification method regulated by national and international constraints. The definition of a rating scale can be described as a combinatorial optimization...
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Dr Simone Roncallo (University of Pavia)06/02/2026, 09:40
Visual information can be manipulated in terms of images, usually captured and then processed through a sequence of computational operations. Alternatively, optical systems can perform such operations directly, reducing computational overhead at the cost of stricter design requirements. We discuss this workflow in the context of quantum technologies. First, we introduce a quantum computational...
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Marco Ludovico Boccanelli (Sapienza Università di Roma)
Classical diffusion models achieve state-of-the-art sample quality and diversity in a wide range of generative tasks. Motivated by this success, several fully quantum and hybrid quantum–classical diffusion schemes have been proposed, yet so far they have mainly been tested on classical data distributions, without clear advantages over classical baselines, or on toy ensembles of quantum states...
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