211 citations · 1.4k across the 42 of their papers we have counts for
42 papers
Quantum Phase Diagram of the D Untruncated SU Lattice Gauge Theory with Dynamical Fermions
Gabriel Rouxinol, Julian Bender, Patrick Emonts +2
Non-Abelian gauge theories with dynamical matter govern the strong interaction and a broad class of strongly correlated quantum systems, yet their ground-state properties remain di…
Quantum error mitigation for Fourier moment computation
Oriel Kiss, Michele Grossi, Alessandro Roggero
Hamiltonian moments in Fourier space - expectation values of the unitary evolution operator under a Hamiltonian at different times - provide a convenient framework to understand qu…
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…
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Paulin de Schoulepnikoff, Oriel Kiss, Sofia Vallecorsa +2
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies…
Qutrit quantum battery: comparing different charging protocols
G. Gemme, M. Grossi, S. Vallecorsa +2
Motivated by recent experimental observations carried out in superconducting transmon circuits, we compare two different charging protocols for three-level quantum batteries based…
Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks
Michele Grossi, Massimiliano Incudini, Mathieu Pellen +1
Extracting longitudinal modes of weak bosons in LHC processes is essential to understand the electroweak-symmetry-breaking mechanism. To that end, we propose a general method, base…
Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors
Roberto Moretti, Marco Rossi, Matteo Biassoni +6
The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by c…
A Full Quantum Generative Adversarial Network Model for High Energy Physics Simulations
Florian Rehm, Sofia Vallecorsa, Michele Grossi +2
The prospect of quantum computing with a potential exponential speed-up compared to classical computing identifies it as a promising method in the search for alternative future Hig…
Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp +5
Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate the…
Quantum Fourier Iterative Amplitude Estimation
Jorge J. Martínez de Lejarza, Michele Grossi, Leandro Cieri +1
Monte Carlo integration is a widely used numerical method for approximating integrals, which is often computationally expensive. In recent years, quantum computing has shown promis…
Quantum Generative Adversarial Networks For Anomaly Detection In High Energy Physics
Elie Bermot, Christa Zoufal, Michele Grossi +4
The standard model (SM) of particle physics represents a theoretical paradigm for the description of the fundamental forces of nature. Despite its broad applicability, the SM does…
A biological sequence comparison algorithm using quantum computers
Büsra Kösoglu-Kind, Robert Loredo, Michele Grossi +3
Genetic information is encoded in a linear sequence of nucleotides, represented by letters ranging from thousands to billions. Mutations refer to changes in the DNA or RNA nucleoti…
Resource Saving via Ensemble Techniques for Quantum Neural Networks
Massimiliano Incudini, Michele Grossi, Andrea Ceschini +4
Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limi…
Bloch Sphere Binary Trees: A method for the visualization of sets of multi-qubit systems pure states
Alice Barthe, Michele Grossi, Jordi Tura +1
Understanding the evolution of a multi-qubit quantum system, or elucidating what portion of the Hilbert space is occupied by a quantum dataset becomes increasingly hard with the nu…
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Julian Schuhmacher, Laura Boggia, Vasilis Belis +7
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 Hadr…
Quantum anomaly detection in the latent space of proton collision events at the LHC
Vasilis Belis, Kinga Anna Woźniak, Ema Puljak +7
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along w…
A novel approach to noisy gates for simulating quantum computers
Giovanni Di Bartolomeo, Michele Vischi, Francesco Cesa +4
We present a novel method for simulating the noisy behaviour of quantum computers, which allows to efficiently incorporate environmental effects in the driven evolution implementin…
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
Massimiliano Incudini, Michele Grossi, Antonio Mandarino +3
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of cla…
Importance sampling for stochastic quantum simulations
Oriel Kiss, Michele Grossi, Alessandro Roggero
Simulating many-body quantum systems is a promising task for quantum computers. However, the depth of most algorithms, such as product formulas, scales with the number of terms in…