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arXiv:2111.07806

Leveraging Quantum Annealer to identify an Event-topology at High Energy Colliders

Minho Kim, Pyungwon Ko, Jae-hyeon Park, Myeonghun Park

Published Nov 15, 2021• 1 citation
arXivHTMLPDF

Abstract

With increasing energy and luminosity available at the Large Hadron collider (LHC), we get a chance to take a pure bottom-up approach solely based on data. This will extend the scope of our understanding about Nature without relying on theoretical prejudices. The required computing resource, however, will increase exponentially with data size and complexities of events if one uses algorithms based on a classical computer. In this letter we propose a simple and well motivated method with a quantum annealer to identify an event-topology, a diagram to describe the history of particles produced at the LHC. We show that a computing complexity can be reduced significantly to the order of polynomials which enables us to decode the "Big" data in a very clear and efficient way. Our method achieves significant improvements in finding a true event-topology, more than by a factor of two compared to a conventional method.

DOI
10.48550/arxiv.2111.07806

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With increasing energy and luminosity available at the Large Hadron collider (LHC), we get a chance to take a pure bottom-up approach solely based on data. This will extend the scope of our understanding about Nature without relying on theoretical prejudices. The required computing resource, however, will increase exponentially with data size and complexities of events if one uses algorithms based on a classical computer. In this letter we propose a simple and well motivated method with a quantum annealer to identify an event-topology, a diagram to describe the history of particles produced at the LHC. We show that a computing complexity can be reduced significantly to the order of polynomials which enables us to decode the "Big" data in a very clear and efficient way. Our method achieves significant improvements in finding a true event-topology, more than by a factor of two compared to a conventional method.
After the next planned upgrades to the LHC, the luminosity it delivers will more than double, substantially increasing the already large demand on computing resources. Therefore an efficient way to reconstruct physical objects is required. Recent studies show that one of the quantum computing techniques, quantum annealing (QA), can be used to perform particle tracking with efficiency higher than 90% in the high pileup region in the high luminosity environment. The algorithm starts by determining the connection between the hits, and classifies the topological objects with their pattern. The current study aims to improve the pre-processing efficiency in the QA-based tracking algorithm by implementing a graph neural network (GNN), which is expected to efficiently generate the topological object needed for the annealing process. Tracking performance with a different setup of the original algorithm is also studied with data collected by the ATLAS experiment.
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.
Experimental High-Energy Physics (HEP), especially the Large Hadron Collider (LHC) programme at the European Organization for Nuclear Research (CERN), is one of the most computationally intensive activities in the world. This demand is set to increase significantly with the upcoming High-Luminosity LHC (HL-LHC), and even more in future machines, such as the Future Circular Collider (FCC). As a consequence, event reconstruction, and in particular jet clustering, is bound to become an even more daunting problem, thus challenging present day computing resources. In this work, we present the first digital quantum algorithm to tackle jet clustering, opening the way for digital quantum processors to address this challenging problem. Furthermore, we show that, at present and future collider energies, our algorithm has comparable, yet generally lower complexity relative to the classical state-of-the-art ktk_tkt​ clustering algorithm.
The search for supersymmetric particles is one of the major goals of the Large Hadron Collider (LHC). Supersymmetric top (stop) searches play a very important role in this respect, but the unprecedented collision rate to be attained at the next high luminosity phase of the LHC poses new challenges for the separation between any new signal and the standard model background. The massive parallelism provided by quantum computing techniques may yield an efficient solution for this problem. In this paper we show a novel application of the zoomed quantum annealing machine learning approach to classify the stop signal versus the background, and implement it in a quantum annealer machine. We show that this approach together with the preprocessing of the data with principal component analysis may yield better results than conventional multivariate approaches.
With the advent of the High-Luminosity Large Hadron Collider (HL-LHC) era, high energy physics (HEP) event selection will require new approaches to rapidly and accurately analyze vast databases. The current study addresses the enormity of HEP databases in an unprecedented manner-a quantum search using Grover's Algorithm (GA) on an unsorted database, ATLAS Open Data, from the ATLAS detector. A novel method to identify rare events at 13 TeV in CERN's LHC using quantum computing (QC) is presented. As indicated by the Higgs boson decay channel [Formula: see text], the detection of four leptons in one event may be used to reconstruct the Higgs boson and, more importantly, evince Higgs boson decay to some new phenomena, such as [Formula: see text]. Searching the dataset for collisions resulting in detection of four leptons using a Jupyter Notebook, a classical simulation of GA, and several quantum computers with multiple qubits, the current application was found to make the proper selection in the unsorted dataset. Quantum search efficacy was analyzed for the incoming HL-LHC by implementing the QC method on multiple classical simulators and IBM's quantum computers with the IBM Qiskit Open Source Software. The current QC application provides a novel, high-efficiency alternative to classical database searches, demonstrating its potential utility as a rapid and increasingly accurate search method in HEP.
The unprecedented increase of complexity and scale of data is expected in the necessary computation for tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching their limits in terms of ambiguities from increasing number of simultaneous collisions, occupancy, and scalability (worse than quadratic), a variety of machine learning approaches to particle track reconstruction are explored. It has been demonstrated previously by HEP.TrkX using TrackML datasets, that graph neural networks, processing events as a graph connecting track measurements, are a promising solution and can reduce the combinatorial background to a manageable amount and are scaling to a computationally reasonable size. In previous work, we have shown a first attempt of Quantum Computing to Graph Neural Networks for track reconstruction of particles. We aim to leverage the capability of quantum computing to evaluate a very large number of states simultaneously and thus to effectively search in a large parameter space. As the next step in this paper, we present an improved model with an iterative approach to overcome the low accuracy convergence of the initial oversimplified Tree Tensor Network (TTN) model.