Long-lived Particles Anomaly Detection with Parametrized Quantum
Circuits
- URL: http://arxiv.org/abs/2312.04238v1
- Date: Thu, 7 Dec 2023 11:50:42 GMT
- Title: Long-lived Particles Anomaly Detection with Parametrized Quantum
Circuits
- Authors: Simone Bordoni, Denis Stanev, Tommaso Santantonio, Stefano Giagu
- Abstract summary: We propose an anomaly detection algorithm based on a parametrized quantum circuit.
This algorithm has been trained on a classical computer and tested with simulations as well as on real quantum hardware.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We investigate the possibility to apply quantum machine learning techniques
for data analysis, with particular regard to an interesting use-case in
high-energy physics. We propose an anomaly detection algorithm based on a
parametrized quantum circuit. This algorithm has been trained on a classical
computer and tested with simulations as well as on real quantum hardware. Tests
on NISQ devices have been performed with IBM quantum computers. For the
execution on quantum hardware specific hardware driven adaptations have been
devised and implemented. The quantum anomaly detection algorithm is able to
detect simple anomalies like different characters in handwritten digits as well
as more complex structures like anomalous patterns in the particle detectors
produced by the decay products of long-lived particles produced at a collider
experiment. For the high-energy physics application, performance is estimated
in simulation only, as the quantum circuit is not simple enough to be executed
on the available quantum hardware. This work demonstrates that it is possible
to perform anomaly detection with quantum algorithms, however, as amplitude
encoding of classical data is required for the task, due to the noise level in
the available quantum hardware, current implementation cannot outperform
classic anomaly detection algorithms based on deep neural networks.
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