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International Conference on High Energy Physics (ICHEP)

Quantum-inspired Machine Learning on high-energy physics data

Marco Trenti, L. Sestini, A. Gianelle, D. Zuliani, Timo Felser, Donatella Lucchesi, Simone Montangero

Published Apr 28, 2020• 1 citation
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Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics. We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3). We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed. Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.
Dr. Ema Puljak Bio: Ema Puljak is a Quantum Algorithms Expert at PlanQC, working on quantum and quantum-inspired machine learning for real-world applications. She completed a collaborative PhD between CERN, the Barcelona Supercomputing Center, and Universitat Autònoma de Barcelona, focusing on improving anomaly detection methods using quantum and tensor-network techniques for medical imaging and high-energy physics. Passionate about science communication, she regularly delivers accessible talks and tutorials on quantum computing, tensor networks, and quantum machine learning. TITLE: Friendly Introduction to Quantum and Quantum-Inspired Machine Learning Abstract: This talk introduces quantum and quantum-inspired machine learning from a practical perspective. We start with a brief overview of classical ML, then move to quantum-inspired approaches based on tensor networks, which already enable efficient representations and compression on classical hardware. Next, we cover quantum machine learning, including variational circuits, quantum kernels, and hybrid quantum–classical workflows, highlighting where quantum models may assist (rather than replace) classical ML. We close with real-world application outlooks for both paradigms and open questions around scalability, and future perspectives, including whether demonstrating quantum advantage should be the only goal of quantum machine learning.