Kavli Affiliate: Masahito Yamazaki
| First 5 Authors: A. Hammad, Mihoko M. Nojiri, Masahito Yamazaki, ,
| Summary:
Anomaly detection is a vital technique for exploring signatures of new
physics Beyond the Standard Model (BSM) at the Large Hadron Collider (LHC). The
vast number of collisions generated by the LHC demands sophisticated deep
learning techniques. Similarity learning, a self-supervised machine learning,
detects anomalous signals by estimating their similarity to background events.
In this paper, we explore the potential of quantum computers for anomaly
detection through similarity learning, leveraging the power of quantum
computing to enhance the known similarity learning method. In the realm of
noisy intermediate-scale quantum (NISQ) devices, we employ a hybrid
classical-quantum network to search for heavy scalar resonances in the di-Higgs
production channel. In the absence of quantum noise, the hybrid network
demonstrates improvement over the known similarity learning method. Moreover,
we employ a clustering algorithm to reduce measurement noise from limited shot
counts, resulting in $9%$ improvement in the hybrid network performance. Our
analysis highlights the applicability of quantum algorithms for LHC data
analysis, where improvements are anticipated with the advent of fault-tolerant
quantum computers.
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