1.2k citations · 7.5k across the 116 of their papers we have counts for
12 papers · 2 filters
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…
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 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…
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…
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…