Quantum machine-learning for SuperSymmetry searches

Project goal

The goal of this project is to develop quantum machine-learning algorithms for the analysis of LHC collision data. The particular example chosen is the classification of SuperSymmetry signals from Standard-Model background.

R&D topic
Quantum technologies
Project coordinator(s)
Koji Terashi (University of Tokyo)
Team members
University of Tokyo: Michiru Kaneda, Tomoe Kishimoto, Masahiko Saito, Ryu Sawada, Junici Tanaka │ CERN openlab: Federico Carminati, Sofia Vallecorsa, Fabio Fracas


Project background

The analysis of LHC data for the detection of effects beyond the Standard Model requires increasing levels of precision. Various machine-learning techniques are now part of the standard analysis toolbox for high-energy physics. Deep-learning algorithms are increasingly demonstrating their usefulness in various areas of analysis, thanks to their ability to explore a much larger dimensional space.

This seems to be an almost ideal area of application for quantum computing, which is offering a potentially enormous parameter space and a correspondingly large level of computational parallelism. Moreover, the quasi-optimal Gibbs sampling features of quantum computers may enhance the training of such deep-learning networks.

Recent progress

During this year the Tokyo group has studied the performance of different quantum variational models for the classification of SuperSymmetry signals from the Standard-Model background, where the signal is: h ➞ ? ±? ∓ ➞ WW (➞lνlν) + ?01?01, and the background comes from: WW (➞lνlν). In particular, the efforts have focused on two approaches: quantum circuit learning (QCL) and quantum variational classifiers (QVC).

The SuperSymmetry data set in the UCI machine-learning repository has been used for this study. A quantum circuit learning with a set of seven variables and a depth of three has been implemented. The results of 5000 iterations with COBYLA (constrained optimisation by linear approximation) minimisation have been compared with classical deep neural networks and and boosted decision trees. Promising results have been achieved. An initial implementation of a QVC, with a depth of two and a set of three input variables, is also being studied.

Next steps

The current results are encouraging. The next steps of this work will be to test the results on real IBM Quantum Experience hardware. Initially, three variables will be used.