Event Classification with Quantum Machine Learning in High-Energy Physics

• URL: http://arxiv.org/abs/2002.09935v2
• Date: Tue, 5 Jan 2021 12:46:51 GMT
• Title: Event Classification with Quantum Machine Learning in High-Energy Physics
• Authors: Koji Terashi, Michiru Kaneda, Tomoe Kishimoto, Masahiko Saito, Ryu Sawada, Junichi Tanaka
• Abstract summary: We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events. We focus on variational quantum approach to learn the properties of input data. We evaluate the performance of the event classification using both simulators and quantum computing devices.
• Score: 0.6291443816903801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum approach to learn the properties of input data and evaluate the performance of the event classification using both simulators and quantum computing devices. Comparison of the performance with standard multi-variate classification techniques based on a boosted-decision tree and a deep neural network using classical computers shows that the quantum algorithm has comparable performance with the standard techniques at the considered ranges of the number of input variables and the size of training samples. The variational quantum algorithm is tested with quantum computers, demonstrating that the discrimination of interesting events from background is feasible. Characteristic behaviors observed during a learning process using quantum circuits with extended gate structures are discussed, as well as the implications of the current performance to the application in high-energy physics experiments.

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)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Large-scale quantum reservoir learning with an analog quantum computer [45.21335836399935]
  We develop a quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning.
  arXiv  Detail & Related papers  (2024-07-02T18:00:00Z)
• 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 data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• A Herculean task: Classical simulation of quantum computers [4.12322586444862]
  This work reviews the state-of-the-art numerical simulation methods that emulate quantum computer evolution under specific operations. We focus on the mainstream state-vector and tensor-network paradigms while briefly mentioning alternative methods.
  arXiv  Detail & Related papers  (2023-02-17T13:59:53Z)
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning [52.77024349608834]
  Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. Key issue is how to address the inherent non-linearity of classical deep learning. We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
  arXiv  Detail & Related papers  (2022-12-22T16:06:24Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Comparing concepts of quantum and classical neural network models for image classification task [0.456877715768796]
  This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network. Although its simulation is time-consuming, the quantum network, although its simulation is time-consuming, overcomes the classical network.
  arXiv  Detail & Related papers  (2021-08-19T18:49:30Z)
• 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)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)

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.