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20242026
most citedQuantum Information meets High-Energy Physics: Input to the update of the European Strategy for Particle Physics

31 citations · 87 across the 13 of their papers we have counts for

collaborators

13 papers

quant-ph2026

Weighted Quantum Signal Processing: Low-Depth Polynomial Approximation with Applications to Kolmogorov-Arnold Networks

Rohit Sarma Sarkar, Rupayan Bhattacharjee, Elias F. Combarro +5

Quantum Signal Processing is a powerful quantum framework for generating and approximating univariate polynomials. However, QSP is often limited by circuit-depth bottlenecks and pa…

hep-lat2026

Neural quantum states for non-Abelian lattice gauge theories with dynamical fermions

Gabriel Rouxinol, Julian Bender, Michele Grossi +2

Determining the ground state of non-Abelian lattice gauge theories coupled to dynamical fermions is key to understanding confinement and the phase structure of gauge--matter system…

quant-ph2026

Physics inspired quantum algorithm for QCD splitting functions

Gabriel Rouxinol, Yacine Haddad, Cenk Tüysüz +2

We introduce a modular quantum circuit primitive to model entanglement dynamics in QCD parton splitting and use it as a composable building block for data-driven, physics-consisten…

hep-ph2026

Coherent Quantum Evaluation of Collider Amplitudes for Effective Field Theory Constraints

Yacine Haddad, Kaidi Xu, Vincent Croft +2

Precision measurements at electron-positron colliders provide stringent tests of the Standard Model and powerful probes of possible higher-dimensional interactions. We present a hy…

quant-ph2025★ 1 cited

Learning Reduced Representations for Quantum Classifiers

Patrick Odagiu, Vasilis Belis, Lennart Schulze +6

Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this im…

cs.LG2025★ 1 cited

Guided Graph Compression for Quantum Graph Neural Networks

Mikel Casals, Vasilis Belis, Elias F. Combarro +3

Graph Neural Networks (GNNs) are effective for processing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix…

hep-ph2025★ 31 cited

Quantum Information meets High-Energy Physics: Input to the update of the European Strategy for Particle Physics

Yoav Afik, Federica Fabbri, Matthew Low +68

Some of the most astonishing and prominent properties of Quantum Mechanics, such as entanglement and Bell nonlocality, have only been studied extensively in dedicated low-energy la…

hep-ph2024★ 23 cited

From angular coefficients to quantum observables: a phenomenological appraisal in di-boson systems

Michele Grossi, Giovanni Pelliccioli, Alessandro Vicini

Motivated by the growing interest in accessing the spin structure of multi-boson processes and in measuring quantum entanglement at high energies, we study polarisation and spin-co…

quant-ph2024★ 6 cited

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Simone Piperno, Andrea Ceschini, Su Yeon Chang +3

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approa…

quant-ph2024★ 5 cited

Latent Style-based Quantum GAN for high-quality Image Generation

Su Yeon Chang, Supanut Thanasilp, Bertrand Le Saux +2

Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images…

quant-ph2024

Operational Framework for a Quantum Database

Carla Rieger, Michele Grossi, Gian Giacomo Guerreschi +2

Databases are an essential component of modern computing infrastructures and allow efficient manipulation of inherently structured data. The structure depends on the type and relat…

quant-ph2024★ 11 cited

Parameterized quantum circuits as universal generative models for continuous multivariate distributions

Alice Barthe, Michele Grossi, Sofia Vallecorsa +2

Parameterized quantum circuits have been extensively used as the basis for machine learning models in regression, classification, and generative tasks. For supervised learning, the…

quant-ph2024★ 9 cited

Guided Quantum Compression for High Dimensional Data Classification

Vasilis Belis, Patrick Odagiu, Michele Grossi +3

Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers…