arXiv subjects

Jacco de Vries

Publications and source records attributed to Jacco de Vries.

4 recordsLinked to original sources

arXiv2026

A 1-bit quantum filter for particle trajectory reconstruction

The transition to the High-Luminosity Large Hadron Collider (HL-LHC) presents a computational challenge where particle reconstruction complexity may outpace classical computing resources. While quantum computing offers potential speedups, standard algorithms like Harrow-Hassidim-Lloyd (HHL) require prohibitive circuit depths for near-term hardware. Here, we introduce the 1-Bit Quantum Filter, a domain-specific adaptation of HHL that reformulates tracking from matrix inversion to binary ground-state filtering. By replacing high-precision phase estimation with a single-ancilla spectral threshold and exploiting the Hamiltonian's sparsity, we achieve an asymptotic gate complexity of $O(\sqrt{N} \log N)$, given Hamiltonian dimension $N$. We validate this approach by simulating LHCb Vertex Locator events with a toy model, and benchmark performance using the noise models of Quantinuum H2 trapped-ion and IBM Heron superconducting processors. This work establishes a resource-efficient track reconstruction method capable of solving realistic event topologies on noise-free simulators and smaller tracking scenarios within the current constraints of the Noisy Intermediate Scale Quantum (NISQ) era.

quant-ph↗

arXiv2023

Searching for ring-like structures in the Cosmic Microwave Background

In this research, we present an alternative methodology to search for ring-like structures in the sky with unusually large temperature gradients, namely Hawking points (HP), in the Cosmic Microwave Background (CMB), which are possible observational effects associated with Conformal Cyclic Cosmology (CCC). To assess the performance of our method, we constructed an artificial data set of HP, according to CCC, and we were able to retrieve $95 \%$ of ring-like anomalies from it. Furthermore, we scanned the \textit{Planck} CMB sky map and compared it to simulations according to $ΛCDM$, where we applied robust statistical tests to assess the existence of HP. Even though no significant ring-like structures were observed, we report the largest excess of HP candidates found at $α= $1\% significance level for the analyzed sky maps (CMB at 70GHz, SEVEM, SMICA, and Commander-Ruler), and we stress the need to continue the theoretical and experimental research in this direction.

astro-ph.CO↗

arXiv2022

Quantum computing for data analysis in high energy physics

Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that often push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated in an effort to understand how the community can benefit from the advantages of quantum information science. In this manuscript, we provide an overview of the state-of-the-art applications of quantum computing to data analysis in HEP, discuss the challenges and opportunities in integrating these novel analysis techniques into a day-to-day analysis workflow, and whether there is potential for a quantum advantage.

physics.data-an↗

arXiv2022

Quantum Machine Learning for $b$-jet charge identification

Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context Quantum Machine Learning is a new and almost unexplored methodology, where the intrinsic properties of quantum computation could be used to exploit particles correlations for improving the jet classification performance. In this paper, we present a brand new approach to identify if a jet contains a hadron formed by a $b$ or $\bar{b}$ quark at the moment of production, based on a Variational Quantum Classifier applied to simulated data of the LHCb experiment. Quantum models are trained and evaluated using LHCb simulation. The jet identification performance is compared with a Deep Neural Network model to assess which method gives the better performance.

hep-ex↗