Tensor Networks
59 papers with code • 0 benchmarks • 0 datasets
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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 literature and an analysis of the use cases that may be affected by such methods.
Certifying 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.
Tensor Network-Constrained Kernel Machines as Gaussian Processes
We analyze the convergence of both CPD and TT-constrained models, and show how TT yields models exhibiting more GP behavior compared to CPD, for the same number of model parameters.
Application 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 qubits, which can be trained to solve various protein-related machine-learning problems.
CompactifAI: 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, substantial energy demands, and limitations for on-site deployment.
A Tensor Network Implementation of Multi Agent Reinforcement Learning
The TN represents a distribution model, where all possible trajectories are considered.
A 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.
Tensor Networks for Explainable Machine Learning in Cybersecurity
In this paper we show how tensor networks help in developing explainability of machine learning algorithms.
Indoor 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.
Tensor 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).
