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Magic-Informed Quantum Architecture Search
Vincenzo Lipardi, Domenica Dibenedetto, Georgios Stamoulis +1
Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed quantum architecture search (…
Nonstabilizerness Estimation using Graph Neural Networks
Vincenzo Lipardi, Domenica Dibenedetto, Georgios Stamoulis +2
This article proposes a Graph Neural Network (GNN) approach to estimate nonstabilizerness in quantum circuits, measured by the stabilizer Rényi entropy (SRE). Nonstabilizerness is…
A 1-bit quantum filter for particle trajectory reconstruction
Xenofon Chiotopoulos, Davide Nicotra, George Scriven +6
The transition to the High-Luminosity Large Hadron Collider (HL-LHC) presents a computational challenge where particle reconstruction complexity may outpace classical computing res…
TrackHHL: A Quantum Computing Algorithm for Track Reconstruction at the LHCb
Xenofon Chiotopoulos, Miriam Lucio Martinez, Davide Nicotra +4
In the future high-luminosity LHC era, high-energy physics experiments face unprecedented computational challenges for event reconstruction. Employing the LHCb vertex locator as a…
Variational Quantum Algorithms for Particle Track Reconstruction
Vincenzo Lipardi, Xenofon Chiotopoulos, Jacco A. de Vries +4
Quantum Computing is a rapidly developing field with the potential to tackle the increasing computational challenges faced in high-energy physics. In this work, we explore the pote…
A Study on Stabilizer Rényi Entropy Estimation using Machine Learning
Vincenzo Lipardi, Domenica Dibenedetto, Georgios Stamoulis +1
Nonstabilizerness is a fundamental resource for quantum advantage, as it quantifies the extent to which a quantum state diverges from those states that can be efficiently simulated…