Tensor Networks
59 papers with code • 0 benchmarks • 0 datasets
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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).
Projecting basis functions with tensor networks for Gaussian process regression
The benefit of our approach comes from the projection to a smaller subspace: It modifies the shape of the basis functions in a way that it sees fit based on the given data, and it allows for efficient computations in the smaller subspace.
Generative 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.
Factorized Tensor Networks for Multi-Task and Multi-Domain Learning
In this paper, we propose a factorized tensor network (FTN) that can achieve accuracy comparable to independent single-task/domain networks with a small number of additional parameters.
Efficient tensor network simulation of IBM's largest quantum processors
Apart from simulating the original experiment for 127 qubits, we also extend our results to 433 and 1121 qubits, and for evolution times around 8 times longer, thus setting a benchmark for the newest IBM quantum machines.
Data 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.
Detecting Violations of Differential Privacy for Quantum Algorithms
Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring.
Quaternion tensor left ring decomposition and application for color image inpainting
Therefore, in this paper, based on the left quaternion matrix multiplication, we propose the quaternion tensor left ring (QTLR) decomposition, which inherits the powerful and generalized representation abilities of the TR decomposition while leveraging the advantages of quaternions for color pixel representation.
Convolutions Through the Lens of Tensor Networks
Despite their simple intuition, convolutions are more tedious to analyze than dense layers, which complicates the generalization of theoretical and algorithmic ideas.
Distributive Pre-Training of Generative Modeling Using Matrix-Product States
Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning.
