Anomaly detection in high-energy physics using a quantum autoencoder

Ngairangbam, Vishal S.; Spannowsky, Michael; Takeuchi, Michihisa

doi:10.1103/physrevd.105.095004

Cited by 54 publications

(27 citation statements)

References 54 publications

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“…O iso k-nearest-neighbors(kNN)-based O iso [1] Autoencoder(AE)-based [14][15][16][17][18][19][20][21][22][23] Graph [24], classical k-means clustering [25] O clu kNN-based O clu [1], TS [13] t-score [2,12,26,27], SOFIE [28], ANODE [29], Poissonian Mixture Model [30] CWoLa [31][32][33][34], TNT [35], SALAD [36] SULU [37] UCluster [38] Table 1. A short summary of the isolation-based and clustering-based novelty evaluators/algorithms.…”

Section: Jhep10(2022)085mentioning

confidence: 99%

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Detecting new physics as novelty — Complementarity matters

Jiang

Juste

et al. 2022

J. High Energ. Phys.

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Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge. In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders. In this paper, we develop an analysis scheme that exploits the complementarity, originally studied in ref. [1], between isolation-based and clustering-based novelty evaluators. This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events. As a further proof of principle, we subsequently apply this scheme to the detection of two significantly different signals at the LHC featuring a $$ t\overline{t}\gamma \gamma $$ t t ¯ γγ final state: $$ t\overline{t}h $$ t t ¯ h , giving a narrow resonance in the diphoton mass spectrum, and gravity-mediated supersymmetry, resulting in broad distributions at high transverse momentum. Compared to existing dedicated searches at the LHC, the sensitivities for detecting both signals are found to be encouraging.

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Section: Jhep10(2022)085mentioning

confidence: 99%

“…[19]. Furthermore, the authors in [23] studied the potential of novelty detection with variational-quantum-circuits-based quantum autoencoder. These proposals are clearly isolation-based, since the novelty response of each testing event is evaluated by them independently.…”

Section: Introductionmentioning

confidence: 99%

Detecting new physics as novelty — Complementarity matters

Jiang

Juste

et al. 2022

J. High Energ. Phys.

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“…In this paper, QML refers to machine learning tasks that are executed on quantum computing hardware. While QML is not known to be more efficient than classical machine learning (CML), there have been many empirical studies to explore the potential of QML for HEP [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] (see also Ref. [20] for a recent review).…”

Section: Introductionmentioning

confidence: 99%

“…The prospect of QML for anomaly detection was first studied in Ref. [16] in the context of autoencoders. These unsupervised tools can be trained without any simulation, but are not as effective as semi-supervised methods when there is a good background model and/or when the new physics is not the lowest density events [64,80].…”

Section: Introductionmentioning

confidence: 99%

Quantum Anomaly Detection for Collider Physics

Alvi¹,

Bauer²,

Nachman³

2022

Preprint

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Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware. While there is currently no formal guarantee that QML is superior to classical ML for relevant problems, there have been many claims of an empirical advantage with high energy physics datasets. These studies typically do not claim an exponential speedup in training, but instead usually focus on an improved performance with limited training data. We explore an analysis that is characterized by a low statistics dataset. In particular, we study an anomaly detection task in the four-lepton final state at the Large Hadron Collider that is limited by a small dataset. We explore the application of QML in a semi-supervised mode to look for new physics without specifying a particular signal model hypothesis. We find no evidence that QML provides any advantage over classical ML. It could be that a case where QML is superior to classical ML for collider physics will be established in the future, but for now, classical ML is a powerful tool that will continue to expand the science of the LHC and beyond.

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“…One of the motivations for using this new technology in HEP relates to the intrinsic properties of quantum computations, namely representing the data in a Hilbert space where the data can be in a superposition of states or in entangled states, which can allow to explore additional information in data analysis and, eventually, contribute to better classification of HEP events, namely in the context of the search for BSM phenomena. Recently, this new technology has been applied to HEP problems such as event reconstruction [4][5][6][7][8][9][10] and classification [11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets

Peixoto¹,

Castro²,

Romão³

et al. 2022

Preprint

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Current quantum systems have significant limitations affecting the processing of large datasets and high dimensionality typical of high energy physics. In this work, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets.

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Anomaly detection in high-energy physics using a quantum autoencoder

Cited by 54 publications

References 54 publications

Detecting new physics as novelty — Complementarity matters

Detecting new physics as novelty — Complementarity matters

Quantum Anomaly Detection for Collider Physics

Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets

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