Christina Reissel

We have developed two quantum classifier models for the $t\bar{t}H(b\bar{b})$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits -- to accommodate for limitations in both simulation hardware and real quantum hardware -- we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.

At HEP experiments, processing billions of records of structured numerical data can be a bottleneck in the analysis pipeline.This step is typically more complex than current query languages allow, such that numerical codes are used.As highly parallel computing architectures are increasingly important in the computing ecosystem, it may be useful to consider how accelerators such as GPUs can be used for data analysis.Using CMS and ATLAS Open Data, we implement a benchmark physics analysis with GPU acceleration directly in Python based on efficient computational kernels using Numba/LLVM, resulting in an order of magnitude throughput increase over a pure CPUbased approach.We discuss the implementation and performance benchmarks of the physics kernels on CPU and GPU targets.We demonstrate how these kernels are combined to a modern ML-intensive workflow to enable efficient data analysis on high-performance servers and remark on possible operational considerations.

After the successful data-taking periods between 2015 and 2018, an important goal of both collaborations, ATLAS and CMS, is the analysis of the full available dataset which provides perfect conditions for precision measurements in the Higgs sector.These proceedings discuss the most recent analyses by the ATLAS and CMS collaborations in which the Higgs boson decays into a pair of bottom quarks or τ leptons among them analyses which drive the sensitivity in the respective channels such as the ATLAS VH(bb) measurement or the H→τ τ CMS results.Moreover, it contains the first differential fiducial Higgs cross section measurement in final states with τ leptons, a result presented by the CMS collaboration at the LHCP2021 conference for the first time.