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arXiv:2312.04238 · Particles

Long-Lived Particles Anomaly Detection with Parametrized Quantum Circuits

Simone Bordoni, Denis Stanev, Tommaso Santantonio, S. Giagu

Published Feb 13, 2023• 14 citations
arXivHTMLPDF

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 was trained on a classical computer and tested with simulations as well as on real quantum hardware. Tests on NISQ devices were performed with IBM quantum computers. For the execution on quantum hardware, specific hardware-driven adaptations were devised and implemented. The quantum anomaly detection algorithm was able to detect simple anomalies such as different characters in handwritten digits as well as more complex structures such as 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, the performance was estimated in simulation only, as the quantum circuit was not simple enough to be executed on the available quantum hardware platform. This work demonstrates that it is possible to perform anomaly detection with quantum algorithms; however, as an amplitude encoding of classical data is required for the task, due to the noise level in the available quantum hardware platform, the current implementation cannot outperform classic anomaly detection algorithms based on deep neural networks.

DOI
10.3390/particles6010016

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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 was trained on a classical computer and tested with simulations as well as on real quantum hardware. Tests on NISQ devices were performed with IBM quantum computers. For the execution on quantum hardware, specific hardware-driven adaptations were devised and implemented. The quantum anomaly detection algorithm was able to detect simple anomalies such as different characters in handwritten digits as well as more complex structures such as 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, the performance was estimated in simulation only, as the quantum circuit was not simple enough to be executed on the available quantum hardware platform. This work demonstrates that it is possible to perform anomaly detection with quantum algorithms; however, as an amplitude encoding of classical data is required for the task, due to the noise level in the available quantum hardware platform, the current implementation cannot outperform classic anomaly detection algorithms based on deep neural networks.
The lack of evidence for new interactions and particles at the Large Hadron Collider (LHC) has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics. Autoencoders are unsupervised machine learning models based on artificial neural networks, capable of learning background distributions. We study quantum autoencoders based on variational quantum circuits for the problem of anomaly detection at the LHC. For a QCD tt‾t\overline{t}tt background and resonant heavy-Higgs signals, we find that a simple quantum autoencoder outperforms classical autoencoders for the same inputs and trains very efficiently. Moreover, this performance is reproducible on present quantum devices. This shows that quantum autoencoders are good candidates for analysing high-energy physics data in future LHC runs.
The standard model (SM) of particle physics represents a theoretical paradigm for the description of the fundamental forces of nature. Despite its broad applicability, the SM does not enable the description of all physically possible events. The detection of events that cannot be described by the SM, which are typically referred to as anomalous, and the related potential discovery of exotic physical phenomena is a non-trivial task. The challenge becomes even greater with next-generation colliders that will produce even more events with additional levels of complexity. The additional data complexity motivates the search for unsupervised anomaly detection methods that do not require prior knowledge about the underlying models. In this work, we develop such a technique. More explicitly, we employ a quantum generative adversarial network to identify anomalous events. The method learns the background distribution from SM data and, then, determines whether a given event is characteristic for the learned background distribution. The proposed quantum-powered anomaly detection strategy is tested on proof-of-principle examples using numerical simulations and IBM Quantum processors. We find that the quantum generative techniques using ten times fewer training data samples can yield comparable accuracy to the classical counterpart for the detection of the Graviton and Higgs particles. Additionally, we empirically compute the capacity of the quantum model and observe an improved expressivity compared to its classical counterpart.
Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition or medical diagnosis. Several algorithms have been introduced using classical computing approaches. However, using quantum computing for solving anomaly detection problems in time series data is a widely unexplored research field. This paper explores the application of quantum autoencoders to time series anomaly detection. We investigate two primary techniques for classifying anomalies: (1) Analyzing the reconstruction error generated by the quantum autoencoder and (2) latent representation analysis. Our simulated experimental results, conducted across various ansaetze, demonstrate that quantum autoencoders consistently outperform classical deep learning-based autoencoders across multiple datasets. Specifically, quantum autoencoders achieve superior anomaly detection performance while utilizing 60-230 times fewer parameters and requiring five times fewer training iterations. In addition, we implement our quantum encoder on real quantum hardware. Our experimental results demonstrate that quantum autoencoders achieve anomaly detection performance on par with their simulated counterparts.
A bstract 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.
Anomaly detection, the task of identifying rare and unusual events within a dataset, is a critical component in numerous applications, including fraud detection, cybersecurity, and industrial monitoring. Traditional anomaly detection methods often rely on classical machine learning algorithms which can be sensitive to noise and may struggle with complex, high-dimensional data. This paper investigates the application of quantum machine learning (QML) algorithms to enhance anomaly detection performance. We propose a framework where anomaly detection problems are formulated as quantum machine learning tasks, leveraging quantum algorithms for data analysis and pattern recognition. The core claim of this work is that QML can significantly improve the precision and efficiency of anomaly detection compared to conventional techniques. Specifically, we explore the potential of quantum support vector machines (QSVMs) and quantum clustering algorithms for anomaly identification. The theoretical underpinnings of these approaches are outlined, and we present a conceptual model demonstrating the transformation of a classical anomaly detection problem into a quantum equivalent. This work lays the groundwork for future research in applying QML to real-world anomaly detection challenges.
Abstract Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition, or medical diagnosis. Several algorithms have been introduced using classical computing approaches. However, using quantum computing for solving anomaly detection problems in time series data is a widely unexplored research field. This paper explores the application of quantum autoencoders to time series anomaly detection. We investigate two primary techniques for classifying anomalies: (1) Analyzing the reconstruction error generated by the quantum autoencoder and (2) latent representation analysis. Our simulated experimental results, conducted across various ansaetze, demonstrate that quantum autoencoders consistently outperform classical deep learning-based autoencoders across multiple datasets. Specifically, quantum autoencoders achieve superior anomaly detection performance while utilizing 60–230 times fewer parameters and requiring five times fewer training iterations. In addition, we implement our quantum encoder on real quantum hardware. Our experimental results demonstrate that quantum autoencoders achieve anomaly detection performance on par with their simulated counterparts.