Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC

• URL: http://arxiv.org/abs/2506.00102v1
• Date: Fri, 30 May 2025 14:18:53 GMT
• Title: Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
• Authors: Ema Puljak, Maurizio Pierini, Artur Garcia-Saez,
• Abstract summary: We propose a tensor network-based strategy for anomaly detection at the LHC.<n>Our results highlight the potential of tensor networks to enhance new-physics discovery.
• Score: 1.092813092010402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum machine learning, which present a promising and efficient alternative for tackling these challenges. In this work, we propose a tensor network-based strategy for anomaly detection at the LHC and demonstrate its superior performance in identifying new phenomena compared to established quantum methods. Our model is a parametrized Matrix Product State with an isometric feature map, processing a latent representation of simulated LHC data generated by an autoencoder. Our results highlight the potential of tensor networks to enhance new-physics discovery.

Related papers

• Quantum similarity learning for anomaly detection [0.0]
  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.
  arXiv  Detail & Related papers  (2024-11-15T03:55:09Z)
• Quantum Pathways for Charged Track Finding in High-Energy Collisions [42.044638679429845]
  In high-energy particle collisions, charged track finding is a complex yet crucial endeavour. We propose a quantum algorithm, specifically quantum template matching, to enhance the accuracy and efficiency of track finding.
  arXiv  Detail & Related papers  (2023-11-01T18:13:59Z)
• Generative Invertible Quantum Neural Networks [0.0]
  Invertible Neural Networks (INNs) have become established tools for the simulation and generation of highly complex data. We propose a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the LHC data of jet-associated production of a Z-boson that decays into leptons. We find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data.
  arXiv  Detail & Related papers  (2023-02-24T21:25:07Z)
• Quantum anomaly detection in the latent space of proton collision events at the LHC [0.7493013403244345]
  We propose a strategy for anomaly detection tasks at the LHC based on unsupervised quantum machine learning.<n>We show that the observed performance enhancement is related to the quantum resources utilised by the model.
  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)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks [72.09574528342732]
  Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs) They often fail to converge to desirable solutions when the target function contains high-frequency features, due to a phenomenon known as spectral bias. In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under gradient descent with momentum (SGDM)
  arXiv  Detail & Related papers  (2022-06-29T19:03:10Z)
• Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach [47.19265172105025]
  We propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN) egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. The architecture is based on a novel mapping from real-world data to Hilbert space.
  arXiv  Detail & Related papers  (2022-01-13T16:35:45Z)
• 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)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• Performance of Particle Tracking Using a Quantum Graph Neural Network [1.0480625205078853]
  The Large Hadron Collider (LHC) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This work explores the possibility of converting a novel Graph Neural Network model, that proven itself for the track reconstruction task, to a Hybrid Graph Neural Network in order to benefit the exponentially growing Hilbert Space.
  arXiv  Detail & Related papers  (2020-12-02T18:23:48Z)
• Quantum-inspired Machine Learning on high-energy physics data [0.0]
  We apply a quantum-inspired machine learning technique to the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from the proton-proton experiment, and how to interpret the classification results.
  arXiv  Detail & Related papers  (2020-04-28T18:00:12Z)
• 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.