QUANTUM MACHINE LEARNING FOR HEP DETECTOR SIMULATIONS

9th International Conference "Distributed Computing and Grid Technologies in Science and Education", pp. 363–368

Abstract

Quantum Machine Learning (qML) is one of the most promising and very intuitive applications onnear-term quantum devices which possess the potential to combat computing resource challengesfaster than traditional computers. Classical Machine Learning (ML) is taking up a significant role inparticle physics to speed up detector simulations. Generative Adversarial Networks (GANs) haveproven to achieve a similar level of accuracy compared to Monte Carlo-based simulations whiledecreasing the computation time by orders of magnitude. In this research we are moving on and applyquantum computing to GAN-based detector simulations.Given the limitations of current quantum hardware in terms of number of qubits, connectivity, andnoise, we perform initial tests with a simplified GAN model running on quantum simulators. Themodel is a classical-quantum hybrid ansatz. It consists of a quantum generator, defined as aparameterised circuit based on single and two qubit gates, combined with a classical discriminator.Our initial qGAN prototype focuses on a one-dimensional toy-distribution, representing the energydeposited in a detector by a single particle. It employs three qubits and achieves high physics accuracythanks to hyper-parameter optimisation. Furthermore, we study the influence of real hardware noisefor the qML GAN training. A second qGAN is developed to simulate 2D images with a 64-pixelresolution, representing the energy deposition patterns in the detector. Different quantum ansatzes arestudied. We obtained the best results using a tree-tensor-network architecture with six qubits.Additionally, we discuss challenges and potential benefits of quantum computing as well as our plansfor future developments.

Authors 4

1. Florian Rehm Aachen

   European Organization for Nuclear Research · RWTH Aachen University

   Affiliation as printed

   CERN , Esplanade des Particules 1 , Geneva , Switzerland

   RWTH Aachen University , Templergraben 55 , Aachen , Germany

   CERN, Esplanade des Particules 1, Geneva, Switzerland

   RWTH Aachen University, Templergraben 55, Aachen, Germany

2. Sofia Vallecorsa

   European Organization for Nuclear Research

   Affiliation as printed

   CERN , Esplanade des Particules 1 , Geneva , Switzerland

   CERN, Esplanade des Particules 1, Geneva, Switzerland

3. K. Borras Aachen

   Deutsches Elektronen-Synchrotron DESY · RWTH Aachen University

   Affiliation as printed

   DESY , Notkestraße 85 , Hamburg , Germany

   RWTH Aachen University , Templergraben 55 , Aachen , Germany

   DESY, Notkestraße 85, Hamburg, Germany

   RWTH Aachen University, Templergraben 55, Aachen, Germany

4. Dirk Krücker

   Deutsches Elektronen-Synchrotron DESY

   Affiliation as printed

   DESY , Notkestraße 85 , Hamburg , Germany

   DESY, Notkestraße 85, Hamburg, Germany