Quantum convolutional neural networks for jet images classification

• URL: http://arxiv.org/abs/2408.08701v1
• Date: Fri, 16 Aug 2024 12:28:10 GMT
• Title: Quantum convolutional neural networks for jet images classification
• Authors: Hala Elhag, Karl Jansen, Lento Nagano, Alice di Tucci,
• Abstract summary: This paper addresses the performance of quantum machine learning in the context of high-energy physics. We use a quantum convolutional neural network (QCNN) for this task and compare its performance with CNN. Our results indicate that QCNN with proper setups tend to perform better than their CNN counterparts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, interest in quantum computing has significantly increased, driven by its potential advantages over classical techniques. Quantum machine learning (QML) exemplifies one of the important quantum computing applications that are expected to surpass classical machine learning in a wide range of instances. This paper addresses the performance of QML in the context of high-energy physics (HEP). As an example, we focus on the top-quark tagging, for which classical convolutional neural networks (CNNs) have been effective but fall short in accuracy when dealing with highly energetic jet images. In this paper, we use a quantum convolutional neural network (QCNN) for this task and compare its performance with CNN using a classical noiseless simulator. We compare various setups for the QCNN, varying the convolutional circuit, type of encoding, loss function, and batch sizes. For every quantum setup, we design a similar setup to the corresponding classical model for a fair comparison. Our results indicate that QCNN with proper setups tend to perform better than their CNN counterparts, particularly when the convolution block has a lower number of parameters. This suggests that quantum models, especially with appropriate encodings, can hold potential promise for enhancing performance in HEP tasks such as top quark jet tagging.

Related papers

• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• Variational Quantum Neural Networks (VQNNS) in Image Classification [0.0]
  This paper investigates how training of quantum neural network (QNNs) can be done using quantum optimization algorithms. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs) VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets which converge the computation in lesser time than QNN with decent training accuracy.
  arXiv  Detail & Related papers  (2023-03-10T11:24:32Z)
• 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)
• Evaluating the performance of sigmoid quantum perceptrons in quantum neural networks [0.0]
  Quantum neural networks (QNN) have been proposed as a promising architecture for quantum machine learning. One candidate is quantum perceptrons designed to emulate the nonlinear activation functions of classical perceptrons. We critically investigate both the capabilities and performance of SQP networks by computing their effective dimension and effective capacity.
  arXiv  Detail & Related papers  (2022-08-12T10:08:11Z)
• Classification of NEQR Processed Classical Images using Quantum Neural Networks (QNN) [0.0]
  This work builds on previous works by the authors and addresses QNN for image classification with Novel Enhanced Quantum Representation of (NEQR) We build an NEQR model circuit to pre-process the same data and feed the images into the QNN. Our results showed marginal improvements (only about 5.0%) where the QNN performance with NEQR exceeded the performance of QNN without NEQR.
  arXiv  Detail & Related papers  (2022-03-29T08:05:53Z)
• Quantum convolutional neural network for classical data classification [0.8057006406834467]
  We benchmark fully parameterized quantum convolutional neural networks (QCNNs) for classical data classification. We propose a quantum neural network model inspired by CNN that only uses two-qubit interactions throughout the entire algorithm.
  arXiv  Detail & Related papers  (2021-08-02T06:48:34Z)
• The dilemma of quantum neural networks [63.82713636522488]
  We show that quantum neural networks (QNNs) fail to provide any benefit over classical learning models. QNNs suffer from the severely limited effective model capacity, which incurs poor generalization on real-world datasets. These results force us to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.
  arXiv  Detail & Related papers  (2021-06-09T10:41:47Z)
• Branching Quantum Convolutional Neural Networks [0.0]
  Small-scale quantum computers are already showing potential gains in learning tasks on large quantum and very large classical data sets. We present a generalization of QCNN, the branching quantum convolutional neural network, or bQCNN, with substantially higher expressibility.
  arXiv  Detail & Related papers  (2020-12-28T19:00:03Z)
• Toward Trainability of Quantum Neural Networks [87.04438831673063]
  Quantum Neural Networks (QNNs) have been proposed as generalizations of classical neural networks to achieve the quantum speed-up. Serious bottlenecks exist for training QNNs due to the vanishing with gradient rate exponential to the input qubit number. We show that QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
  arXiv  Detail & Related papers  (2020-11-12T08:32:04Z)
• Quantum Deformed Neural Networks [83.71196337378022]
  We develop a new quantum neural network layer designed to run efficiently on a quantum computer. It can be simulated on a classical computer when restricted in the way it entangles input states.
  arXiv  Detail & Related papers  (2020-10-21T09:46:12Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.