Results 11 to 20 of about 21,996,514 (252)
Multi-Modal Image Fusion Based on Matrix Product State of Tensor [PDF]
Frontiers in Neurorobotics, 2021 Multi-modal image fusion integrates different images of the same scene collected by different sensors into one image, making the fused image recognizable by the computer and perceived by human vision easily.
Yixiang Lu +4 more
doaj +2 more sources
Optimising Matrix Product State Simulations of Shor's Algorithm [PDF]
Quantum, 2019 We detail techniques to optimise high-level classical simulations of Shor's quantum factoring algorithm. Chief among these is to examine the entangling properties of the circuit and to effectively map it across the one-dimensional structure of a matrix ...
Aidan Dang +2 more
doaj +3 more sources
Residual matrix product state for machine learning [PDF]
SciPost Physics, 2023 Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning. Nevertheless, there still exists a considerable accuracy gap between tensor network and the sophisticated neural network ...
Ye-Ming Meng, Jing Zhang, Peng Zhang, Chao Gao, Shi-Ju Ran
doaj +3 more sources
Scalar one-point functions and matrix product states of AdS/dCFT
Physics Letters B, 2018 We determine in a closed form all scalar one-point functions of the defect CFT dual to the D3–D5 probe brane system with k units of flux which amounts to calculating the overlap between a Bethe eigenstate of the integrable SO(6) spin chain and a certain ...
Marius de Leeuw +2 more
doaj +3 more sources
Quantum state preparation of normal distributions using matrix product states [PDF]
npj Quantum InformationState preparation is a necessary component of many quantum algorithms. In this work, we combine a method for efficiently representing smooth differentiable probability distributions using matrix product states with recently discovered techniques for ...
Jason Iaconis +2 more
doaj +2 more sources
Machine Learning Matrix Product State Ansatz for strongly correlated systems [PDF]
, 2022 Machine learning (ML) has been used to optimize the matrix product state (MPS) ansatz for wavefunction of strongly correlated systems. The ML optimization of MPS has been tested for Heisenberg Hamiltonian on one-dimensional and ladder lattices which ...
Debashree, Ghosh +3 more
core +2 more sources
Multipartite Correlations in Quantum Collision Models
Entropy, 2022 Quantum collision models have proved to be useful for a clear and concise description of many physical phenomena in the field of open quantum systems: thermalization, decoherence, homogenization, nonequilibrium steady state, entanglement generation ...
Sergey Filippov
doaj +1 more source
Explainable natural language processing with matrix product states
New Journal of Physics, 2022 Despite empirical successes of recurrent neural networks (RNNs) in natural language processing (NLP), theoretical understanding of RNNs is still limited due to intrinsically complex non-linear computations.
Jirawat Tangpanitanon +5 more
doaj +1 more source
Tangent-space methods for truncating uniform MPS
SciPost Physics Core, 2021 A central primitive in quantum tensor network simulations is the problem of approximating a matrix product state with one of a lower bond dimension.
Bram Vanhecke, Maarten Van Damme, Jutho Haegeman, Laurens Vanderstraeten, Frank Verstraete
doaj +1 more source
Stack Operation of Tensor Networks
Frontiers in Physics, 2022 The tensor network, as a factorization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction, and stacking.
Tianning Zhang +4 more
doaj +1 more source