Papers Tensor Networks
“Tensor Networks” 태그가 달린 논문 226편 · 필터 해제
Quantum-inspired Techniques in Tensor Networks for Industrial Contexts
In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literat…
Tensor NetworksTensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
Algorithms developed to solve many-body quantum problems, like tensor networks, can turn into powerful quantum-inspired tools to tackle problems in the classical domain. In this work, we focus on matrix product operators…
Tensor NetworksLearning parameter dependence for Fourier-based option pricing with tensor trains
A long-standing issue in mathematical finance is the speed-up of option pricing, especially for multi-asset options. A recent study has proposed to use tensor train learning algorithms to speed up Fourier transform (FT)-…
Tensor NetworksCertifying almost all quantum states with few single-qubit measurements
Certifying that an n-qubit state synthesized in the lab is close to the target state is a fundamental task in quantum information science. However, existing rigorous protocols either require deep quantum circuits or expo…
AllBenchmarkingTensor NetworksTensor Network-Constrained Kernel Machines as Gaussian Processes
Tensor Networks (TNs) have recently been used to speed up kernel machines by constraining the model weights, yielding exponential computational and storage savings. In this paper we prove that the outputs of Canonical Po…
Gaussian ProcessesTensor NetworksApplication of Quantum Tensor Networks for Protein Classification
We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubit…
Binary ClassificationClassificationLanguage ModellingProtein Language Model+1Técnicas Quantum-Inspired en Tensor Networks para Contextos Industriales
In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literat…
Tensor NetworkstnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)
Tensor networks are efficient for extremely high-dimensional representation, but their model selection, known as tensor network structure search (TN-SS), is a challenging problem. Although several works have targeted TN-…
Image CompressionModel SelectionTensor NetworksCompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, sub…
Model CompressionQuantizationTensor NetworksA Tensor Network Implementation of Multi Agent Reinforcement Learning
Recently it has been shown that tensor networks (TNs) have the ability to represent the expected return of a single-agent finite Markov decision process (FMDP). The TN represents a distribution model, where all possible …
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningTensor NetworksA quatum inspired neural network for geometric modeling
By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance. In particular, their effective message-passin…
Tensor NetworksTensor Networks for Explainable Machine Learning in Cybersecurity
In this paper we show how tensor networks help in developing explainability of machine learning algorithms. Specifically, we develop an unsupervised clustering algorithm based on Matrix Product States (MPS) and apply it …
ClusteringTensor NetworksIndoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies
This paper proposes an approach to build 3D scene graphs in arbitrary indoor and outdoor environments. Such extension is challenging; the hierarchy of concepts that describe an outdoor environment is more complex than fo…
3d scene graph generationgraph constructionGraph GenerationLanguage Modelling+4Tensor networks for interpretable and efficient quantum-inspired machine learning
It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML). Tensor network (TN), which is a well-established mathematical tool originatin…
Tensor NetworksProjecting basis functions with tensor networks for Gaussian process regression
This paper presents a method for approximate Gaussian process (GP) regression with tensor networks (TNs). A parametric approximation of a GP uses a linear combination of basis functions, where the accuracy of the approxi…
Bayesian InferenceregressionTensor NetworksGenerative Learning of Continuous Data by Tensor Networks
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning. While possessing …
Automated Theorem ProvingTensor NetworksFactorized Tensor Networks for Multi-Task and Multi-Domain Learning
Multi-task and multi-domain learning methods seek to learn multiple tasks/domains, jointly or one after another, using a single unified network. The key challenge and opportunity is to exploit shared information across t…
Tensor NetworksTensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization. However, the standard optimization techniques used for training the cont…
Binary ClassificationQuantum Machine LearningTensor NetworksEfficient tensor network simulation of IBM's largest quantum processors
We show how quantum-inspired 2d tensor networks can be used to efficiently and accurately simulate the largest quantum processors from IBM, namely Eagle (127 qubits), Osprey (433 qubits) and Condor (1121 qubits). We simu…
Tensor NetworksData is often loadable in short depth: Quantum circuits from tensor networks for finance, images, fluids, and proteins
Though there has been substantial progress in developing quantum algorithms to study classical datasets, the cost of simply \textit{loading} classical data is an obstacle to quantum advantage. When the amplitude encoding…
Tensor Networks