1.2k citations · 7.5k across the 116 of their papers we have counts for
119 papers
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…
Kubernetes-Orchestrated Hybrid Quantum-Classical Workflows
Mar Tejedor, Michele Grossi, Cenk Tüysüz +2
Hybrid quantum-classical workflows combine quantum processing units (QPUs) with classical hardware to address computational tasks that are challenging or infeasible for conventiona…
Hybrid Method of Efficient Simulation of Physics Applications for a Quantum Computer
Carla Rieger, Albert T. Schmitz, Gehad Salem +5
Quantum chemistry and materials science are among the most promising areas for demonstrating algorithmic quantum advantage and quantum utility due to their inherent quantum mechani…
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…
Physics Briefing Book: Input for the 2026 update of the European Strategy for Particle Physics
Jorge de Blas, Monica Dunford, Emanuele Bagnaschi +127
The European Strategy for Particle Physics (ESPP) reflects the vision and presents concrete plans of the European particle physics community for advancing human knowledge in fundam…
Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
Sebastian Macaluso, Giovanni Geraci, Elías F. Combarro +4
The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum compu…
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…
Roughening and dynamics of an electric flux string in a (2+1)D lattice gauge theory
Francesco Di Marcantonio, Sunny Pradhan, Sofia Vallecorsa +2
We investigate the roughening transition in the pure lattice gauge theory in (2+1) dimensions. Using numerical simulations with matrix product states, we explore the…
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…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
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…
Order Parameter Discovery for Quantum Many-Body Systems
Nicola Mariella, Tara Murphy, Francesco Di Marcantonio +4
Quantum phase transitions reveal deep insights into the behavior of many-body quantum systems, but identifying these transitions without prior knowledge of order parameters remains…
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…
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…
Exploring the Phase Diagram of the quantum one-dimensional ANNNI model
M. Cea, M. Grossi, S. Monaco +3
This work presents a circuit-level integration of Quantum Machine Learning and Tensor Networks in the context of the one-dimensional Axial Next-Nearest-Neighbor Ising model with a…
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…
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…
Symmetry breaking in geometric quantum machine learning in the presence of noise
Cenk Tüysüz, Su Yeon Chang, Maria Demidik +3
Geometric quantum machine learning based on equivariant quantum neural networks (EQNN) recently appeared as a promising direction in quantum machine learning. Despite the encouragi…
Loop Feynman integration on a quantum computer
Jorge J. Martínez de Lejarza, Leandro Cieri, Michele Grossi +2
This work investigates in detail the performance and advantages of a new quantum Monte Carlo integrator, dubbed Quantum Fourier Iterative Amplitude Estimation (QFIAE), to numerical…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
Counterdiabatic optimized driving in quantum phase sensitive models
Francesco Pio Barone, Oriel Kiss, Michele Grossi +2
State preparation plays a pivotal role in numerous quantum algorithms, including quantum phase estimation. This paper extends and benchmarks counterdiabatic driving protocols acros…
Approximately Equivariant Quantum Neural Network for Group Symmetries in Images
Su Yeon Chang, Michele Grossi, Bertrand Le Saux +1
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of n…
Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
Alberto Di Meglio, Karl Jansen, Ivano Tavernelli +43
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage…
Precise Image Generation on Current Noisy Quantum Computing Devices
Florian Rehm, Sofia Vallecorsa, Kerstin Borras +3
The Quantum Angle Generator (QAG) is a new full Quantum Machine Learning model designed to generate accurate images on current Noise Intermediate Scale (NISQ) Quantum devices. Vari…