Quantum similarity learning for anomaly detection

• URL: http://arxiv.org/abs/2411.09927v1
• Date: Fri, 15 Nov 2024 03:55:09 GMT
• Title: Quantum similarity learning for anomaly detection
• Authors: A. Hammad, Mihoko M. Nojiri, Masahito Yamazaki,
• Abstract summary: We explore the potential of quantum computers for anomaly detection through similarity learning. In the realm of noisy intermediate-scale quantum devices, we employ a hybrid classical-quantum network to search for heavy scalar resonances. Our analysis highlights the applicability of quantum algorithms for LHC data analysis, where improvements are anticipated with the advent of fault-tolerant quantum computers.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Exploring Quantum-Enhanced Machine Learning for Computer Vision: Applications and Insights on Noisy Intermediate-Scale Quantum Devices [0.0]
  This study explores the intersection of quantum computing and Machine Learning (ML) It evaluates the effectiveness of hybrid quantum-classical algorithms, such as the data re-uploading scheme and the patch Generative Adversarial Networks (GAN) model, on small-scale quantum devices.
  arXiv  Detail & Related papers  (2024-04-01T20:55:03Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Quantum anomaly detection in the latent space of proton collision events at the LHC [1.0480625205078853]
  We propose a new strategy for anomaly detection at the LHC based on unsupervised quantum machine learning algorithms. For kernel-based anomaly detection, we identify a regime where the quantum model significantly outperforms its classical counterpart. We demonstrate that the observed consistent performance advantage is related to the inherent quantum properties of the circuit used.
  arXiv  Detail & Related papers  (2023-01-25T19:00:01Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks [68.8204255655161]
  We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
  arXiv  Detail & Related papers  (2022-06-29T14:34:00Z)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)
• Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier [0.0]
  We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. We find that this algorithm has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method.
  arXiv  Detail & Related papers  (2020-10-14T18:05:49Z)
• Robustness Verification of Quantum Classifiers [1.3534683694551501]
  We define a formal framework for the verification and analysis of quantum machine learning algorithms against noises. A robust bound is derived and an algorithm is developed to check whether or not a quantum machine learning algorithm is robust with respect to quantum training data. Our approach is implemented on Google's Quantum classifier and can verify the robustness of quantum machine learning algorithms with respect to a small disturbance of noises.
  arXiv  Detail & Related papers  (2020-08-17T11:56:23Z)
• Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions [0.5249805590164901]
  We numerically simulate the noisy hybrid quantum generative adversarial networks (HQGANs) to learn continuous probability distributions. We also investigate the effect of different parameters on the training time to reduce the computational scaling of the algorithm. Our results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate scale quantum devices.
  arXiv  Detail & Related papers  (2020-06-02T23:14:45Z)
• Entanglement Classification via Neural Network Quantum States [58.720142291102135]
  In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
  arXiv  Detail & Related papers  (2019-12-31T07:40:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.