Quantum support vector machines for Higgs boson classification

Quantum support vector machines for Higgs boson classification

Project goal

This project is investigating the use of quantum support vector machines (QSVMs) for the classification of particle collision events that produce a certain type of decay for the Higgs boson. Specifically, such machines are being used to identify instances where a Higgs boson fluctuates for a very short time into a top quark and a top anti-quark, before decaying into two photons. Understanding this process — known by physicists as ttH production — is challenging, as it is rare: only 1% of Higgs bosons are produced in association with two top quarks and, in addition, the Higgs and the top quarks decay into other particles in many complex ways, or modes.

R&D topic
Quantum technologies
Project coordinator(s)
Sau-Lan Wu (Wisconsin University), Ivano Tavernelli (IBM Zurich), Federico Carminati (CERN openlab)
Team members
University of Wisconsin: Chen Zhou, Shaujun San, Wen Guan │ IBM Zurich: Panagiotis Barkoutsos, Jennifer Glick

Collaborators

Project background

QSVMs are among the most promising machine-learning algorithms for quantum computers. Initial quantum implementations have already shown performances comparable to their classical counterparts. QSVMs are considered suitable algorithms for early adoption on noisy, near-term quantum-computing devices. Several initiatives are studying and optimising input data representation and training strategies.

We are testing IBM’s QSVM algorithm within the ATLAS experiment. Today, identifying ttH-production events relies on classical support vector machines, as well as another machine-learning technique known as ‘boosted decision trees’. Classically, these methods are used to improve event selection and background rejection by analysing 47 high level characteristic features.

Recent progress

We are working to compare the QSVM to the classical approach in terms of classification accuracy. We are also working to ascertain the level of resources needed for training the model (time to convergence and training dataset size) and studying how different types of noise affect the final performance. In order to do this, we are making use of the IBM’s quantum simulator, with support from their expert team. Preliminary results, obtained using the quantum simulator, show that the QSVM can achieve performance that is comparable to its classical counterpart in terms of accuracy, while being much faster. We are now simulating noise in different ways, in order to understand performance on real hardware.

Next steps

Testing the algorithm on real hardware is one of the primary challenges. At the same time, we continue to work on the optimisation of the QSVM accuracy and we are studying the robustness of the algorithm against noise.

 

 


Presentations

    W. Guan, Application on LHC High Energy Physic data analysis with IBM Quantum Computing (March). Presented at 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT), Saas-Fee, 2019. http://cern.ch/go/6DnG