A Novel Quantum Realization of Jet Clustering in High-Energy Physics Experiments

• URL: http://arxiv.org/abs/2407.09056v2
• Date: Thu, 3 Oct 2024 01:53:27 GMT
• Title: A Novel Quantum Realization of Jet Clustering in High-Energy Physics Experiments
• Authors: Yongfeng Zhu, Weifeng Zhuang, Chen Qian, Yunheng Ma, Dong E. Liu, Manqi Ruan, Chen Zhou,
• Abstract summary: 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.
• Score: 8.841173525787223
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Exploring the application of quantum technologies to fundamental sciences holds the key to fostering innovation for both sides. 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 and forms the basis for studying properties of the Higgs boson, which underlies teh mechanism of mass generation for subatomic particles. For the first time, by mapping collision events into graphs--with particles as nodes and their angular separations as edges--we realize jet clustering using the Quantum Approximate Optimization Algorithm (QAOA), a hybrid quantum-classical algorithm for addressing classical combinatorial optimization problems with available quantum resources. Our results, derived from 30 qubits on quantum computer simulator and 6 qubits on quantum computer hardware, demonstrate that jet clustering performance with QAOA is comparable with or even better than classical algorithms for a small-sized problem. This study highlights the feasibility of quantum computing to revolutionize jet clustering, bringing the practical application of quantum computing in high-energy physics experiments one step closer.

Related papers

• Hybrid quantum-classical approach for combinatorial problems at hadron colliders [7.2572969510173655]
  We explore the potential of quantum algorithms to resolve the problems in particle physics experiments. We consider top quark pair production in the fully hadronic channel at the Large Hadron Collider. We show that the efficiency for selecting the correct pairing is greatly improved by utilizing quantum algorithms.
  arXiv  Detail & Related papers  (2024-10-29T18:00:07Z)
• 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)
• Universal Euler-Cartan Circuits for Quantum Field Theories [0.0]
  A hybrid quantum-classical algorithm is presented for the computation of non-perturbative characteristics of quantum field theories. The algorithm relies on a universal parametrized quantum circuit ansatz based on Euler and Cartan's decompositions of single and two-qubit operators.
  arXiv  Detail & Related papers  (2024-07-31T01:59:09Z)
• 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)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC [5.747925022035578]
  We have pioneered employing a quantum machine learning algorithm to study the $e+e- rightarrow ZH$ process at the Circular Electron-Positron Collider (CEPC) Using 6 qubits on quantum computer simulators, we optimised the QSVM- Kernel algorithm and obtained a classification performance similar to the classical support-vector machine algorithm. Our study shows that state-of-the-art quantum computing technologies could be utilised by particle physics.
  arXiv  Detail & Related papers  (2022-09-26T15:46:30Z)
• 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)
• Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
  Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems. We propose an algorithm to implement imaginary time propagation on a quantum computer.
  arXiv  Detail & Related papers  (2021-02-24T12:48:00Z)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)

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.