Tensor Networks
59 papers with code • 0 benchmarks • 0 datasets
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Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently.
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection
pavliclab/aaai2022-clear2022-visual_relationship_detection-rwfn
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AAAI Workshop CLeaR 2022
Furthermore, background knowledge represented by RWFNs can be used to alleviate the incompleteness of training sets even though the space complexity of RWFNs is much smaller than LTNs (1:27 ratio).
lambeq: An Efficient High-Level Python Library for Quantum NLP
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP).
Differentiable Programming of Isometric Tensor Networks
xwkgch/IsoTensor
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Differentiable programming is a new programming paradigm which enables large scale optimization through automatic calculation of gradients also known as auto-differentiation.
Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74 % in the evaluation on the Causality Treebank.
Faster-LTN: a neuro-symbolic, end-to-end object detection architecture
The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation.
Tensor networks for unsupervised machine learning
bnuliujing/tn-for-unsup-ml
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Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models such as restricted Boltzmann machines and neural networks.
Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States
Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems.
Segmenting two-dimensional structures with strided tensor networks
We use the matrix product state (MPS) tensor network on non-overlapping patches of a given input image to predict the segmentation mask by learning a pixel-wise linear classification rule in a high dimensional space.
General tensor network decoding of 2D Pauli codes
Specifically, we propose a decoder that approximates maximally likelihood decoding for 2D stabiliser and subsystem codes subject to Pauli noise.
