Tensor network algorithms provide exact optimal task assignments on machines under directed constraints, with preprocessing and iterative improvements to reduce complexity.
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abstract
TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. We demonstrate the use of the API with applications both physics and machine learning, with details appearing in companion papers.
verdicts
UNVERDICTED 3representative citing papers
TensorFlow-backed TensorNetwork implementation of MERA for critical 1D Ising model with conformal data extraction and 200x GPU acceleration reported.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
citing papers explorer
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Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective
Tensor network algorithms provide exact optimal task assignments on machines under directed constraints, with preprocessing and iterative improvements to reduce complexity.
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TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models
TensorFlow-backed TensorNetwork implementation of MERA for critical 1D Ising model with conformal data extraction and 200x GPU acceleration reported.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.