Papers Tensor Networks
“Tensor Networks” 태그가 달린 논문 226편 · 필터 해제
On Faster Marginalization with Squared Circuits via Orthonormalization
Squared tensor networks (TNs) and their generalization as parameterized computational graphs -- squared circuits -- have been recently used as expressive distribution estimators in high dimensions. However, the squaring …
Tensor NetworksComb Tensor Networks vs. Matrix Product States: Enhanced Efficiency in High-Dimensional Spaces
Modern approaches to generative modeling of continuous data using tensor networks incorporate compression layers to capture the most meaningful features of high-dimensional inputs. These methods, however, rely on traditi…
Tensor NetworksUltra-low latency quantum-inspired machine learning predictors implemented on FPGA
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning (ML) tasks, yielding comparable results …
QuantizationTensor NetworksLTNtorch: PyTorch Implementation of Logic Tensor Networks
Logic Tensor Networks (LTN) is a Neuro-Symbolic framework that effectively incorporates deep learning and logical reasoning. In particular, LTN allows defining a logical knowledge base and using it as the objective of a …
Binary ClassificationLogical ReasoningTensor NetworksAnomaly Detection from a Tensor Train Perspective
We present a series of algorithms in tensor networks for anomaly detection in datasets, by using data compression in a Tensor Train representation. These algorithms consist of preserving the structure of normal data in c…
Anomaly DetectionData CompressionTensor NetworksLearning to Classify Quantum Phases of Matter with a Few Measurements
We study the identification of quantum phases of matter, at zero temperature, when only part of the phase diagram is known in advance. Following a supervised learning approach, we show how to use our previous knowledge t…
Generalization BoundsTensor NetworksFourier Neural Operators for Learning Dynamics in Quantum Spin Systems
Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective approach for simulating the time evolutio…
Tensor NetworksAQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling
The growing computational demands of artificial intelligence (AI) in addressing climate change raise significant concerns about inefficiencies and environmental impact, as highlighted by the Jevons paradox. We propose an…
Tensor NetworksLearning out-of-time-ordered correlators with classical kernel methods
Out-of-Time Ordered Correlators (OTOCs) are widely used to investigate information scrambling in quantum systems. However, directly computing OTOCs with classical computers is an expensive procedure. This is due to the n…
Tensor NetworksA Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems
Quantum many-body systems are of great interest for many research areas, including physics, biology and chemistry. However, their simulation is extremely challenging, due to the exponential growth of the Hilbert space wi…
AI AgentTensor NetworksA Matrix Product State Model for Simultaneous Classification and Generation
Quantum machine learning (QML) is a rapidly expanding field that merges the principles of quantum computing with the techniques of machine learning. One of the powerful mathematical frameworks in this domain is tensor ne…
Quantum Machine LearningTensor NetworksCoarse-To-Fine Tensor Trains for Compact Visual Representations
The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial suc…
3D ReconstructionDenoisingNovel View SynthesisTensor NetworksQuantum consistent neural/tensor networks for photonic circuits with strongly/weakly entangled states
Modern quantum optical systems such as photonic quantum computers and quantum imaging devices require great precision in their designs and implementations in the hope to realistically exploit entanglement and reach a rea…
parameter estimationTensor NetworksLearning topological states from randomized measurements using variational tensor network tomography
Learning faithful representations of quantum states is crucial to fully characterizing the variety of many-body states created on quantum processors. While various tomographic methods such as classical shadow and MPS tom…
Tensor NetworksSisteme Hibride de Invatare Automata si Aplicatii
In this paper, a deep neural network approach and a neuro-symbolic one are proposed for classification and regression. The neuro-symbolic predictive models based on Logic Tensor Networks are capable of discriminating and…
Tensor NetworksProbabilistic Inference in the Era of Tensor Networks and Differential Programming
Probabilistic inference is a fundamental task in modern machine learning. Recent advances in tensor network (TN) contraction algorithms have enabled the development of better exact inference methods. However, many common…
Tensor NetworksCons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints
In this study, we introduce a novel family of tensor networks, termed constrained matrix product states (MPS), designed to incorporate exactly arbitrary discrete linear constraints, including inequalities, into sparse bl…
Combinatorial OptimizationTensor NetworksFederated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare
Healthcare industries frequently handle sensitive and proprietary data, and due to strict privacy regulations, they are often reluctant to share data directly. In today's context, Federated Learning (FL) stands out as a …
Federated LearningTensor NetworksBarren Plateaus in Variational Quantum Computing
Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) phenomenon. When a model exhibits…
Learning TheoryTensor NetworksTensor cumulants for statistical inference on invariant distributions
Many problems in high-dimensional statistics appear to have a statistical-computational gap: a range of values of the signal-to-noise ratio where inference is information-theoretically possible, but (conjecturally) compu…
Tensor Networks