Survey on Computational Applications of Tensor Network Simulations

• URL: http://arxiv.org/abs/2408.05011v1
• Date: Fri, 9 Aug 2024 11:46:47 GMT
• Title: Survey on Computational Applications of Tensor Network Simulations
• Authors: Marcos Díez García, Antonio Márquez Romero,
• Abstract summary: Review aims to clarify which classes of relevant applications have been proposed for which class of tensor networks. We intend this review to be a high-level tour on tensor network applications which is easy to read by non-experts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tensor networks are a popular and computationally efficient approach to simulate general quantum systems on classical computers and, in a broader sense, a framework for dealing with high-dimensional numerical problems. This paper presents a broad literature review of state-of-the-art applications of tensor networks and related topics across many research domains including: machine learning, mathematical optimisation, materials science, quantum chemistry and quantum circuit simulation. This review aims to clarify which classes of relevant applications have been proposed for which class of tensor networks, and how these perform compared with other classical or quantum simulation methods. We intend this review to be a high-level tour on tensor network applications which is easy to read by non-experts, focusing on key results and limitations rather than low-level technical details of tensor networks.

Related papers

• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• A Linear Algebraic Framework for Dynamic Scheduling Over Memory-Equipped Quantum Networks [2.5168553347063862]
  This work deals with the problem of scheduling in an arbitrary entanglement swapping quantum network. We introduce a linear algebraic framework that exploits quantum memory through the creation of intermediate entangled links. An additional class of Max-Weight inspired policies is proposed and benchmarked, reducing significantly the cost at the price of a slight performance degradation.
  arXiv  Detail & Related papers  (2023-07-12T08:41:17Z)
• Tensor Networks or Decision Diagrams? Guidelines for Classical Quantum Circuit Simulation [65.93830818469833]
  tensor networks and decision diagrams have independently been developed with differing perspectives, terminologies, and backgrounds in mind. We consider how these techniques approach classical quantum circuit simulation, and examine their (dis)similarities with regard to their most applicable abstraction level. We provide guidelines for when to better use tensor networks and when to better use decision diagrams in classical quantum circuit simulation.
  arXiv  Detail & Related papers  (2023-02-13T19:00:00Z)
• 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)
• Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning [0.0]
  We introduce a copy node' method that successfully initializes arbitrary tensor networks. We present numerical results that show that the combination of techniques presented here produces quantum inspired tensor network models.
  arXiv  Detail & Related papers  (2022-03-03T19:00:40Z)
• A Practical Guide to the Numerical Implementation of Tensor Networks I: Contractions, Decompositions and Gauge Freedom [0.0]
  We present an overview of the key ideas and skills necessary to begin implementing tensor network methods numerically. The topics presented are of key importance to many common tensor network algorithms such as DMRG, TEBD, TRG, PEPS and MERA.
  arXiv  Detail & Related papers  (2022-02-04T14:10:09Z)
• Loop-Free Tensor Networks for High-Energy Physics [0.0]
  tensor network methods are a powerful theoretical and numerical paradigm spawning from condensed matter physics and quantum information science. This brief review introduces the reader to tensor network methods, a powerful theoretical and numerical paradigm spawning from condensed matter physics and quantum information science.
  arXiv  Detail & Related papers  (2021-09-24T09:38:45Z)
• On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification [88.31717434938338]
  The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts.
  arXiv  Detail & Related papers  (2021-09-20T12:41:50Z)
• Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference [56.24109486973292]
  We study the interplay between pruning and quantization during the training of neural networks for ultra low latency applications. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task.
  arXiv  Detail & Related papers  (2021-02-22T19:00:05Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)
• Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation [1.4416132811087747]
  We propose a parallel algorithm for the contraction of tensor networks using probabilistic models. We apply the resulting algorithm to the simulation of random quantum circuits.
  arXiv  Detail & Related papers  (2020-04-22T23:00:42Z)

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.