Adiabatic Quantum Algorithm for Multijet Clustering in High Energy Physics

• URL: http://arxiv.org/abs/2012.14514v1
• Date: Mon, 28 Dec 2020 22:45:19 GMT
• Title: Adiabatic Quantum Algorithm for Multijet Clustering in High Energy Physics
• Authors: Diogo Pires, Yasser Omar and Jo\~ao Seixas
• Abstract summary: This paper introduces a novel quantum binary clustering algorithm to tackle dijet event clustering. The benchmarked efficiency is of the order of $96%$, thus yielding substantial improvements over the current quantum state-of-the-art. We also show how to generalize the proposed objective function into a more versatile form, capable of solving the clustering problem in multijet events.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The currently predicted increase in computational demand for the upcoming High-Luminosity Large Hadron Collider (HL-LHC) event reconstruction, and in particular jet clustering, is bound to challenge present day computing resources, becoming an even more complex combinatorial problem. In this paper, we show that quantum annealing can tackle dijet event clustering by introducing a novel quantum annealing binary clustering algorithm. The benchmarked efficiency is of the order of $96\%$, thus yielding substantial improvements over the current quantum state-of-the-art. Additionally, we also show how to generalize the proposed objective function into a more versatile form, capable of solving the clustering problem in multijet events.

Related papers

• Quantum-annealing-inspired algorithms for multijet clustering [0.0]
  We introduce novel quantum-annealing-inspired algorithms for clustering multiple jets in electron-positron collision events. One of these quantum-annealing-inspired algorithms, ballistic simulated bifurcation, overcomes problems previously observed in multijet clustering with quantum-annealing approaches.
  arXiv  Detail & Related papers  (2024-10-18T07:31:04Z)
• A Novel Quantum Realization of Jet Clustering in High-Energy Physics Experiments [8.841173525787223]
  In high-energy particle collisions, quarks and gluons are produced and immediately form collimated particle sprays known as jets. Accurate jet clustering is crucial as it retains the information of the originating quark or gluon. This study highlights the feasibility of quantum computing to revolutionize jet clustering.
  arXiv  Detail & Related papers  (2024-07-12T07:26:22Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Charged particle reconstruction for future high energy colliders with Quantum Approximate Optimization Algorithm [0.0]
  The charged particle reconstruction, the so-called track reconstruction, can be considered as a quadratic unconstrained binary optimization problem. The Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising algorithms to solve such problems and to seek for a quantum advantage. It is found that the QAOA shows promising performance and demonstrated itself as one of the candidates for the track reconstruction using quantum computers.
  arXiv  Detail & Related papers  (2023-10-16T10:26:37Z)
• Quantum Vision Clustering [10.360126989185261]
  We present the first clustering formulation tailored for resolution using Adiabatic quantum computing. The proposed approach demonstrates high competitiveness compared to state-of-the-art optimization-based methods. This work showcases the solvability of the proposed clustering problem on current-generation real quantum computers for small examples.
  arXiv  Detail & Related papers  (2023-09-18T16:15:16Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• A hybrid quantum-classical algorithm for multichannel quantum scattering of atoms and molecules [62.997667081978825]
  We propose a hybrid quantum-classical algorithm for solving the Schr"odinger equation for atomic and molecular collisions. The algorithm is based on the $S$-matrix version of the Kohn variational principle, which computes the fundamental scattering $S$-matrix. We show how the algorithm could be scaled up to simulate collisions of large polyatomic molecules.
  arXiv  Detail & Related papers  (2023-04-12T18:10:47Z)
• Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
  We design, implement, and evaluate three hybrid quantum k-Means algorithms. We exploit quantum phenomena to speed up the computation of distances. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
  arXiv  Detail & Related papers  (2022-12-13T16:04:16Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Quantum Annealing for Jet Clustering with Thrust [0.0]
  In high-energy collider physics, quantum-assisted algorithms might accelerate the clustering of particles into jets. We find that quantum annealing yields similar performance to exact classical approaches. Without tuning, comparable performance can be obtained through a hybrid quantum/classical approach.
  arXiv  Detail & Related papers  (2022-05-05T17:46:07Z)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.