Problem
Research questions and friction points this paper is trying to address.
Quantum Tensor Network
MPS
Optimization Methods
Gradient Descent
DMRG
Quantum Tensor Network Learning with DMRG
🤖 AI Summary
研究通过引入全局归一化条件，使用梯度下降和DMRG两种方法优化MPS，以解决量子态表示问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantum Tensor Network
Matrix Product State (MPS)
Global Normalization
Gradient Descent
DMRG
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