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Learning Generative Models for Active Inference using Tensor Networks
The ability of tensor networks to represent the probabilistic nature of quantum states as well as to reduce large state spaces makes tensor networks a natural candidate for active inference.
A scaling hypothesis for projected entangled-pair states
We introduce a new paradigm for scaling simulations with projected entangled-pair states (PEPS) for critical strongly-correlated systems, allowing for reliable extrapolations of PEPS data with relatively small bond dimensions $D$.
Quantum Physics
Solving frustrated Ising models using tensor networks
Motivated by the recent success of tensor networks to calculate the residual entropy of spin ice and kagome Ising models, we develop a general framework to study frustrated Ising models in terms of infinite tensor networks %, i. e. tensor networks that can be contracted using standard algorithms for infinite systems.
Tensor Networks
Statistical Mechanics
Quantum Physics
