Mixed State Entanglement Classification using Artificial Neural Networks

• URL: http://arxiv.org/abs/2102.06053v1
• Date: Thu, 11 Feb 2021 14:59:24 GMT
• Title: Mixed State Entanglement Classification using Artificial Neural Networks
• Authors: Cillian Harney, Mauro Paternostro, Stefano Pirandola
• Abstract summary: Separable Neural Network Quantum States employs a neural network inspired parameterisation of quantum states whose entanglement properties are explicitly programmable. We extend the use of SNNS to mixed, multipartite states, providing a versatile and efficient tool for the investigation of intricately entangled quantum systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable methods for the classification and quantification of quantum entanglement are fundamental to understanding its exploitation in quantum technologies. One such method, known as Separable Neural Network Quantum States (SNNS), employs a neural network inspired parameterisation of quantum states whose entanglement properties are explicitly programmable. Combined with generative machine learning methods, this ansatz allows for the study of very specific forms of entanglement which can be used to infer/measure entanglement properties of target quantum states. In this work, we extend the use of SNNS to mixed, multipartite states, providing a versatile and efficient tool for the investigation of intricately entangled quantum systems. We illustrate the effectiveness of our method through a number of examples, such as the computation of novel tripartite entanglement measures, and the approximation of ultimate upper bounds for qudit channel capacities.

Related papers

• Measurement-based quantum machine learning [0.0]
  A quantum neural network (QNN) is an object that extends the notion of a classical neural network to quantum models for quantum data. We propose a universal QNN in this framework which we call the multiple-triangle ansatz (MuTA)
  arXiv  Detail & Related papers  (2024-05-14T05:17:01Z)
• 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)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Simulating quantum circuits using the multi-scale entanglement renormalization ansatz [0.0]
  We propose a scalable technique for approximate simulations of intermediate-size quantum circuits. We benchmark the proposed technique for checkerboard-type intermediate-size quantum circuits of 27 qubits with various depths.
  arXiv  Detail & Related papers  (2021-12-28T09:05:01Z)
• Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
  We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
  arXiv  Detail & Related papers  (2021-08-30T23:50:05Z)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks [2.962453125262748]
  gradient-free approach for training multi-layered neural networks based upon quantum perceptrons. We make use of measurable operators to define the states of the network in a manner consistent with a Markov process.
  arXiv  Detail & Related papers  (2020-09-23T01:38:34Z)
• Solving Quantum Master Equations with Deep Quantum Neural Networks [0.0]
  We use deep quantum feedforward neural networks capable of universal quantum computation to represent the mixed states for open quantum many-body systems. Owning to the special structure of the quantum networks, this approach enjoys a number of notable features, including the absence of barren plateaus.
  arXiv  Detail & Related papers  (2020-08-12T18:00:08Z)
• Entanglement Classification via Neural Network Quantum States [58.720142291102135]
  In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
  arXiv  Detail & Related papers  (2019-12-31T07:40:23Z)

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.