Hi all,
Tomorrow Laura Boggia will tell us about her masterthesis she completed at IBM with Ivano Tavernelli, entitled 'Quantum Machine Learning for Anomaly Detection in High Energy Physics'. See below for the abstract. The talk will take place at 2pm in F31.1 or on Zoom: https://ethz.zoom.us/j/362994444.
Best, Ladina
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Abstract:
The advent of quantum computers gave rise to research for new applications that can be efficiently executed with the use of a quantum processor. Quantum Machine Learning (QML) is a prominent application for quantum devices and is described as the intersection between machine learning and quantum computation. High Energy Physics (HEP), as a computationally intensive domain of research, is a natural candidate for the application of QML. The search of new physics beyond the Standard Model (SM), that was initiated after the discovery of the Higgs particle, can potentially benefit from this new computing paradigm. We investigate the use of a QML protocol, namely Quantum Support Vector Machine (QSVM), for unravelling physics beyond the processes that can be described by the SM. The main differentiation of a QSVM relies on the use of a quantum feature map for the embedding of the input data via quantum circuits that can be efficiently evaluated on a quantum computer. In our work, we start from data sets containing simulated SM events and we randomly distort the data to introduce anomalies. Training on these two classes of data, we prepare a QML model that can classify anomalies on a complete data set coming from an Large Hadron Collider (LHC) experiment (SM and anomalies included). We perform a parametric analysis and identify how different parameters of the QSVM influence the performance of our workflow and determine an optimal parameter configuration for our use case. We demonstrate the potential of QSVM to better classify and identify patterns that will eventually lead to the understanding of new physics in HEP experiments. We confirm our findings with the use of a data set containing anomalous events caused by a graviton or Higgs particle and we also perform an experiment on an IBM quantum computer available in the cloud.
Hi all,
Todays seminar will start at 14:15 (15min. later than usual). It will still be in the same room F31.1 or on Zoom https://ethz.zoom.us/j/362994444.
Best, Ladina
On 2 May 2022, at 08:43, Ladina Hausmann hladina@phys.ethz.ch wrote:
Hi all,
Tomorrow Laura Boggia will tell us about her masterthesis she completed at IBM with Ivano Tavernelli, entitled 'Quantum Machine Learning for Anomaly Detection in High Energy Physics'. See below for the abstract. The talk will take place at 2pm in F31.1 or on Zoom: https://ethz.zoom.us/j/362994444.
Best, Ladina
%%%%%
Abstract:
The advent of quantum computers gave rise to research for new applications that can be efficiently executed with the use of a quantum processor. Quantum Machine Learning (QML) is a prominent application for quantum devices and is described as the intersection between machine learning and quantum computation. High Energy Physics (HEP), as a computationally intensive domain of research, is a natural candidate for the application of QML. The search of new physics beyond the Standard Model (SM), that was initiated after the discovery of the Higgs particle, can potentially benefit from this new computing paradigm. We investigate the use of a QML protocol, namely Quantum Support Vector Machine (QSVM), for unravelling physics beyond the processes that can be described by the SM. The main differentiation of a QSVM relies on the use of a quantum feature map for the embedding of the input data via quantum circuits that can be efficiently evaluated on a quantum computer. In our work, we start from data sets containing simulated SM events and we randomly distort the data to introduce anomalies. Training on these two classes of data, we prepare a QML model that can classify anomalies on a complete data set coming from an Large Hadron Collider (LHC) experiment (SM and anomalies included). We perform a parametric analysis and identify how different parameters of the QSVM influence the performance of our workflow and determine an optimal parameter configuration for our use case. We demonstrate the potential of QSVM to better classify and identify patterns that will eventually lead to the understanding of new physics in HEP experiments. We confirm our findings with the use of a data set containing anomalous events caused by a graviton or Higgs particle and we also perform an experiment on an IBM quantum computer available in the cloud.
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