Machine learning-based classification of vector vortex beams

• URL: http://arxiv.org/abs/2005.07949v1
• Date: Sat, 16 May 2020 10:58:49 GMT
• Title: Machine learning-based classification of vector vortex beams
• Authors: Taira Giordani and Alessia Suprano and Emanuele Polino and Francesca Acanfora and Luca Innocenti and Alessandro Ferraro and Mauro Paternostro and Nicol\`o Spagnolo and Fabio Sciarrino
• Abstract summary: We show a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods to recognize and classify specific polarization patterns.
• Score: 48.7576911714538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -- namely convolutional neural networks and principal component analysis -- to recognize and classify specific polarization patterns. Our study demonstrates the significant advantages resulting from the use of machine learning-based protocols for the construction and characterization of high-dimensional resources for quantum protocols.

Related papers

• Hybrid Quantum-Classical Machine Learning with String Diagrams [49.1574468325115]
  This paper develops a formal framework for describing hybrid algorithms in terms of string diagrams. A notable feature of our string diagrams is the use of functor boxes, which correspond to a quantum-classical interfaces.
  arXiv  Detail & Related papers  (2024-07-04T06:37:16Z)
• Learning equivariant tensor functions with applications to sparse vector recovery [5.557442038265024]
  We focus on equivariant functions with respect to the diagonal action of the Lorentz and symplectic groups. Our goal behind these characterizations is to define equivariant machine learning models.
  arXiv  Detail & Related papers  (2024-06-03T17:32:43Z)
• 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)
• Equivariant Quantum Graph Circuits [10.312968200748116]
  We propose equivariant quantum graph circuits (EQGCs) as a class of parameterized quantum circuits with strong inductive bias for learning over graph-structured data. Our theoretical perspective on quantum graph machine learning methods opens many directions for further work, and could lead to models with capabilities beyond those of classical approaches.
  arXiv  Detail & Related papers  (2021-12-10T00:14:12Z)
• Quantum Alphatron: quantum advantage for learning with kernels and noise [2.94944680995069]
  We provide quantum versions of the Alphatron in the fault-tolerant setting. We discuss the quantum advantage in the context of learning of two-layer neural networks.
  arXiv  Detail & Related papers  (2021-08-26T09:36:20Z)
• Quantum Machine Learning with SQUID [64.53556573827525]
  We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
  arXiv  Detail & Related papers  (2021-04-30T21:34:11Z)
• Lorentz Group Equivariant Neural Network for Particle Physics [58.56031187968692]
  We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group. For classification tasks in particle physics, we demonstrate that such an equivariant architecture leads to drastically simpler models that have relatively few learnable parameters.
  arXiv  Detail & Related papers  (2020-06-08T17:54:43Z)
• word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data [2.63067287928779]
  We propose two theoretical approaches for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
  arXiv  Detail & Related papers  (2020-03-27T18:23:55Z)
• Spectra of Perfect State Transfer Hamiltonians on Fractal-Like Graphs [62.997667081978825]
  We study the spectral features, on fractal-like graphs, of Hamiltonians which exhibit the special property of perfect quantum state transfer. The essential goal is to develop the theoretical framework for understanding the interplay between perfect quantum state transfer, spectral properties, and the geometry of the underlying graph.
  arXiv  Detail & Related papers  (2020-03-25T02:46:14Z)

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.