Interferometric Neural Networks

• URL: http://arxiv.org/abs/2310.16742v1
• Date: Wed, 25 Oct 2023 16:17:47 GMT
• Title: Interferometric Neural Networks
• Abstract summary: We introduce neural networks composed of interferometers and then build generative networks from them. Our networks do not have any classical layer and can be realized on quantum computers or photonic chips. In multi-class image classification tasks, our networks achieve accuracies of 93% and 83%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: On the one hand, artificial neural networks have many successful applications in the field of machine learning and optimization. On the other hand, interferometers are integral parts of any field that deals with waves such as optics, astronomy, and quantum physics. Here, we introduce neural networks composed of interferometers and then build generative adversarial networks from them. Our networks do not have any classical layer and can be realized on quantum computers or photonic chips. We demonstrate their applicability for combinatorial optimization, image classification, and image generation. For combinatorial optimization, our network consistently converges to the global optimum or remains within a narrow range of it. In multi-class image classification tasks, our networks achieve accuracies of 93% and 83%. Lastly, we show their capability to generate images of digits from 0 to 9 as well as human faces.

Related papers

• An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images [2.1659912179830023]
  Quantum neural networks are deemed suitable to replace classical neural networks in their ability to learn and scale up network models. Medical image classification pertains well to applications in deep learning, particularly, convolutional neural networks.
  arXiv  Detail & Related papers  (2024-09-24T10:43:27Z)
• Hybrid Quantum-Classical Photonic Neural Networks [0.0]
  We show a combination of classical network layers with trainable continuous variable quantum circuits. On a classification task, hybrid networks achieve the same performance when benchmarked against fully classical networks that are twice the size.
  arXiv  Detail & Related papers  (2024-07-02T15:31:38Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Image segmentation with traveling waves in an exactly solvable recurrent neural network [71.74150501418039]
  We show that a recurrent neural network can effectively divide an image into groups according to a scene's structural characteristics. We present a precise description of the mechanism underlying object segmentation in this network. We then demonstrate a simple algorithm for object segmentation that generalizes across inputs ranging from simple geometric objects in grayscale images to natural images.
  arXiv  Detail & Related papers  (2023-11-28T16:46:44Z)
• Unsupervised Domain Transfer with Conditional Invertible Neural Networks [83.90291882730925]
  We propose a domain transfer approach based on conditional invertible neural networks (cINNs) Our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood. Our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks.
  arXiv  Detail & Related papers  (2023-03-17T18:00:27Z)
• Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition [77.95361323613147]
  Current state-of-the-art visual recognition systems rely on pretraining a neural network on a large-scale dataset and finetuning the network weights on a smaller dataset. In this work, we prove that dynamically adapting network architectures tailored for each domain task along with weight finetuning benefits in both efficiency and effectiveness. Our method can be easily generalized to an unsupervised paradigm by replacing supernet training with self-supervised learning in the source domain tasks and performing linear evaluation in the downstream tasks.
  arXiv  Detail & Related papers  (2021-03-31T08:15:17Z)
• Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks [50.684661759340145]
  Firefly neural architecture descent is a general framework for progressively and dynamically growing neural networks. We show that firefly descent can flexibly grow networks both wider and deeper, and can be applied to learn accurate but resource-efficient neural architectures. In particular, it learns networks that are smaller in size but have higher average accuracy than those learned by the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-17T04:47:18Z)
• 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks [0.0]
  We demonstrate a universal optical vector convolutional accelerator operating beyond 10 TeraFLOPS (floating point operations per second) We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.
  arXiv  Detail & Related papers  (2020-11-14T21:24:01Z)
• Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks [78.65792427542672]
  Dynamic Graph Network (DG-Net) is a complete directed acyclic graph, where the nodes represent convolutional blocks and the edges represent connection paths. Instead of using the same path of the network, DG-Net aggregates features dynamically in each node, which allows the network to have more representation ability.
  arXiv  Detail & Related papers  (2020-10-02T16:50:26Z)
• Deep neural networks for the evaluation and design of photonic devices [0.0]
  Review: How deep neural networks can learn from training sets and operate as high-speed surrogate electromagnetic solvers. Fundamental data sciences framed within the context of photonics will also be discussed.
  arXiv  Detail & Related papers  (2020-06-30T19:52:54Z)
• Tensor Networks for Medical Image Classification [0.456877715768796]
  We focus on the class of Networks, which has been a work horse for physicists in the last two decades to analyse quantum many-body systems. We extend the Matrix Product State tensor networks to be useful in medical image analysis tasks. We show that tensor networks are capable of attaining performance that is comparable to state-of-the-art deep learning methods.
  arXiv  Detail & Related papers  (2020-04-21T15:02:58Z)

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.