Entanglement Structure Detection via Computer Vision

• URL: http://arxiv.org/abs/2401.03400v1
• Date: Sun, 7 Jan 2024 07:11:22 GMT
• Title: Entanglement Structure Detection via Computer Vision
• Authors: Rui Li, Junling Du, Zheng Qin, Shikun Zhang, Chunxiao Du, Yang Zhou and Zhisong Xiao
• Abstract summary: Quantum entanglement plays a pivotal role in various quantum information processing tasks. We propose a hybrid CNN-Transformer model for both the classification of GHZ and W states and the detection of various entanglement structures.
• Score: 18.876952671920133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum entanglement plays a pivotal role in various quantum information processing tasks. However, there still lacks a universal and effective way to detecting entanglement structures, especially for high-dimensional and multipartite quantum systems. Noticing the mathematical similarities between the common representations of many-body quantum states and the data structures of images, we are inspired to employ advanced computer vision technologies for data analysis. In this work, we propose a hybrid CNN-Transformer model for both the classification of GHZ and W states and the detection of various entanglement structures. By leveraging the feature extraction capabilities of CNNs and the powerful modeling abilities of Transformers, we can not only effectively reduce the time and computational resources required for the training process but also obtain high detection accuracies. Through numerical simulation and physical verification, it is confirmed that our hybrid model is more effective than traditional techniques and thus offers a powerful tool for independent detection of multipartite entanglement.

Related papers

• Quantum Multimodal Contrastive Learning Framework [0.0]
  We propose a novel framework for multimodal contrastive learning utilizing a quantum encoder to integrate EEG (electroencephalogram) and image data. We demonstrate that the quantum encoder effectively captures intricate patterns within EEG signals and image features, facilitating improved contrastive learning across modalities.
  arXiv  Detail & Related papers  (2024-08-25T19:08:43Z)
• High-dimentional Multipartite Entanglement Structure Detection with Low Cost [18.876952671920133]
  We propose a neural network model to generate representations suitable for entanglement structure detection. Our method achieves over 95% detection accuracy for up to 19 qubits systems.
  arXiv  Detail & Related papers  (2024-08-23T12:09:26Z)
• Quantum Vision Transformers for Quark-Gluon Classification [3.350407101925898]
  We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits. We evaluate our method by applying the model to multi-detector jet images from CMS Open Data.
  arXiv  Detail & Related papers  (2024-05-16T17:45:54Z)
• Symmetry-invariant quantum machine learning force fields [0.0]
  We design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning.
  arXiv  Detail & Related papers  (2023-11-19T16:15:53Z)
• Simulation of Entanglement Generation between Absorptive Quantum Memories [56.24769206561207]
  We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe. We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
  arXiv  Detail & Related papers  (2022-12-17T05:51:17Z)
• Vision Transformer with Convolutions Architecture Search [72.70461709267497]
  We propose an architecture search method-Vision Transformer with Convolutions Architecture Search (VTCAS) The high-performance backbone network searched by VTCAS introduces the desirable features of convolutional neural networks into the Transformer architecture. It enhances the robustness of the neural network for object recognition, especially in the low illumination indoor scene.
  arXiv  Detail & Related papers  (2022-03-20T02:59:51Z)
• Post-Training Quantization for Vision Transformer [85.57953732941101]
  We present an effective post-training quantization algorithm for reducing the memory storage and computational costs of vision transformers. We can obtain an 81.29% top-1 accuracy using DeiT-B model on ImageNet dataset with about 8-bit quantization.
  arXiv  Detail & Related papers  (2021-06-27T06:27:22Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Detecting quantum entanglement with unsupervised learning [5.136040801991848]
  In this work, we exploit the convexity of normal samples without quantum features and design an unsupervised machine learning method to detect quantum features as anomalies. We propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, indicating that our work could provide a powerful tool to extract quantum features hidden in high-dimensional quantum data.
  arXiv  Detail & Related papers  (2021-03-08T14:56:24Z)
• Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer [55.41644538483948]
  We implement a quantum-circuit based generative model to learn and sample the prior distribution of a Generative Adversarial Network. We train this hybrid algorithm on an ion-trap device based on $171$Yb$+$ ion qubits to generate high-quality images.
  arXiv  Detail & Related papers  (2020-12-07T18:51:28Z)
• Entanglement transfer, accumulation and retrieval via quantum-walk-based qubit-qudit dynamics [50.591267188664666]
  Generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies. We propose a protocol that is able to attain entangled states of $d$-dimensional systems through a quantum-walk-based it transfer & accumulate mechanism. In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons.
  arXiv  Detail & Related papers  (2020-10-14T14:33:34Z)

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.