Neural-network approach for identifying nonclassicality from click-counting data

• URL: http://arxiv.org/abs/2003.01605v2
• Date: Thu, 14 May 2020 08:48:30 GMT
• Title: Neural-network approach for identifying nonclassicality from click-counting data
• Authors: Valentin Gebhart, Martin Bohmann
• Abstract summary: We present an artificial neural network approach for the identification of nonclassical states of light based on recorded measurement statistics. In particular, we implement and train a network which is capable of recognizing nonclassical states based on the click statistics recorded with multiplexed detectors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine-learning and neural-network approaches have gained huge attention in the context of quantum science and technology in recent years. One of the most essential tasks for the future development of quantum technologies is the verification of nonclassical resources. Here, we present an artificial neural network approach for the identification of nonclassical states of light based on recorded measurement statistics. In particular, we implement and train a network which is capable of recognizing nonclassical states based on the click statistics recorded with multiplexed detectors. We use simulated data for training and testing the network, and we show that it is capable of identifying some nonclassical states even if they were not used in the training phase. Especially, in the case of small sample sizes, our approach can be more sensitive in identifying nonclassicality than established criteria which suggests possible applications in presorting of experimental data and online applications.

Related papers

• ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
  We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems. We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
  arXiv  Detail & Related papers  (2023-08-22T09:11:53Z)
• Neural Networks for Programming Quantum Annealers [6.531395267592592]
  Quantum machine learning has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. In this study, we consider a similar but not quite the same case, where a classical fully-fledged neural network is connected with a small quantum annealer. We simulate this system to learn several common datasets, including those for image and sound recognition.
  arXiv  Detail & Related papers  (2023-08-13T16:43:07Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• 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)
• Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection [54.83707803301847]
  The research was conducted on a Formspring dataset provided in a Kaggle competition on automatic cyberbullying detection. The study confirmed the effectiveness of Neural Networks in cyberbullying detection and the correlation between classifier performance and Feature Density.
  arXiv  Detail & Related papers  (2022-06-04T03:17:15Z)
• Flexible learning of quantum states with generative query neural networks [4.540894342435848]
  We show that learning across multiple quantum states can be achieved by a generative query neural network. Our network can be trained offline with classically simulated data, and later be used to characterize unknown quantum states from real experimental data.
  arXiv  Detail & Related papers  (2022-02-14T15:48:27Z)
• Quantum Self-Supervised Learning [22.953284192004034]
  We propose a hybrid quantum-classical neural network architecture for contrastive self-supervised learning. We apply our best quantum model to classify unseen images on the ibmq_paris quantum computer.
  arXiv  Detail & Related papers  (2021-03-26T18:00:00Z)
• Identifying nonclassicality from experimental data using artificial neural networks [52.77024349608834]
  We train an artificial neural network to classify classical and nonclassical states from their quadrature-measurement distributions. We show that the network is able to correctly identify classical and nonclassical features from real experimental quadrature data for different states of light.
  arXiv  Detail & Related papers  (2021-01-18T15:12:47Z)
• Identifying Learning Rules From Neural Network Observables [26.96375335939315]
  We show that different classes of learning rules can be separated solely on the basis of aggregate statistics of the weights, activations, or instantaneous layer-wise activity changes. Our results suggest that activation patterns, available from electrophysiological recordings of post-synaptic activities, may provide a good basis on which to identify learning rules.
  arXiv  Detail & Related papers  (2020-10-22T14:36:54Z)

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.