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Quantum Networks
Quantum Machine Learning
Quantum Simulation
Quantum Tensor Network Learning with DMRG
arXiv
Authors: Gustav J L Jäger, Martin B Plenio, Hans-Martin Rieser
Year
2026
Paper ID
76643
Status
Preprint
Abstract Read
~2 min
Abstract Words
87
Citations
N/A
Abstract
Tensor Networks are a relatively new machine learning approach. The architectures proposed initially are inspired by approaches from quantum many-body physics simulations. One common layout is the matrix product state (MPS) also known as a tensor train optimized with gradient descent techniques. We introduce a global normalization condition, so that the MPS represents a quantum state. We investigate two optimization methods that find the locally optimal tensors and compare them regarding their effectiveness. One is based on gradient descent and the other on an adaptation of DMRG.
Why This Paper Matters
- This paper contributes to the Quantum Networks research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- Tensor Networks are a relatively new machine learning approach.
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