Quantum Algorithms for Charged Particle Track Reconstruction in the LUXE Experiment

The LUXE experiment is a new experiment in planning in Hamburg, which will study Quantum Electrodynamics at the strong-field frontier. LUXE intends to measure the positron production rate in this unprecedented regime by using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally expensive for classical computers. This paper investigates the potential future use of gate-based quantum computers for pattern recognition in track reconstruction. Approaches based on a quadratic unconstrained binary optimisation and a quantum graph neural network are investigated in classical simulations of quantum devices and compared with a classical track reconstruction algorithm. In addition, a proof-of-principle study is performed using quantum hardware.

[1]  B. Heinemann,et al.  Track reconstruction at the LUXE experiment using quantum algorithms , 2022, 2210.13021.

[2]  B. Heinemann,et al.  Studying quantum algorithms for particle track reconstruction in the LUXE experiment , 2022, Journal of Physics: Conference Series.

[3]  H. Gray Quantum pattern recognition algorithms for charged particle tracking , 2021, Philosophical Transactions of the Royal Society A.

[4]  D. Dobos,et al.  Hybrid quantum classical graph neural networks for particle track reconstruction , 2021, Quantum Machine Intelligence.

[5]  D. Rousseau,et al.  A Common Tracking Software Project , 2021, Computing and Software for Big Science.

[6]  B. King,et al.  From local to nonlocal: higher fidelity simulations of photon emission in intense laser pulses , 2021, New Journal of Physics.

[7]  M. Spiropulu,et al.  Performance of a geometric deep learning pipeline for HL-LHC particle tracking , 2021, The European Physical Journal C.

[8]  B. Heinemann,et al.  Conceptual design report for the LUXE experiment , 2021, The European Physical Journal Special Topics.

[9]  Samah Mohamed Saeed,et al.  A Lightweight Approach to Detect Malicious/Unexpected Changes in the Error Rates of NISQ Computers , 2020, 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD).

[10]  Jay M. Gambetta,et al.  Mitigating measurement errors in multiqubit experiments , 2020, 2006.14044.

[11]  Keisuke Fujii,et al.  Sequential minimal optimization for quantum-classical hybrid algorithms , 2019, Physical Review Research.

[12]  Heather Gray,et al.  A Pattern Recognition Algorithm for Quantum Annealers , 2019, Computing and Software for Big Science.

[13]  Paolo Calafiura,et al.  Quantum Associative Memory in Hep Track Pattern Recognition , 2019, EPJ Web of Conferences.

[14]  Prabhat,et al.  Novel deep learning methods for track reconstruction , 2018, 1810.06111.

[15]  E. Farhi,et al.  A Quantum Approximate Optimization Algorithm , 2014, 1411.4028.

[16]  Alán Aspuru-Guzik,et al.  A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.

[17]  P. Billoir Track fitting with multiple scattering: A new method , 1984 .

[18]  N. B. Narozhnyi Propagation of Plane Electromagnetic Waves in a Constant Field , 1969 .

[19]  A. I. Nikishov,et al.  Quantum Processes in the Field of a Plane Electromagnetic Wave and in a Constant Field 1 , 1964 .

[20]  L. Brown,et al.  Interaction of Intense Laser Beams with Electrons , 1964 .

[21]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[22]  Julian Schwinger,et al.  On gauge invariance and vacuum polarization , 1951 .

[23]  G. Breit,et al.  Collision of Two Light Quanta , 1934 .

[24]  W. Marsden I and J , 2012 .

[25]  H. Reiss Absorption of Light by Light , 1962 .