The number of edges (or wires) connecting to a tensor is equal to that tensor’s rank. When an index (edge) is contracted by combining two tensors according to Eq 1, the two tensor are replaced by a new one. The number of scalar entries in the tensor scales exponentially in the number of edges to which it connects. In general it is not trivial to choose an efficient contraction ordering that minimizes the total number of floating point operations.</p
In many applications, it is needed to change the topology of a tensor network directly and without a...
Tensor networks are powerful factorization techniques which reduce resource requirements for numeric...
A tensor network is a type of decomposition used to express and approximate large arrays of data. A ...
Contracting tensor networks is often computationally demanding. Well-designed contraction sequences ...
We present a conceptually clear and algorithmically useful framework for parameterizing the costs of...
For each tensor network, the number of tensors (|V|), edges (|E|), and optimal contraction complexit...
Tensor network contraction is central to problems ranging from many-body physics to computer science...
The computational cost of contracting a tensor network depends on the sequence of contractions, but ...
The computational cost of contracting a tensor network depends on the sequence of contractions, but ...
We introduce a method for transforming low-order tensors into higher-order tensors and apply it to ...
We introduce a method for transforming low-order tensors into higher-order tensors and apply it to t...
textabstractWe introduce a method for transforming low-order tensors into higher-order tensors and ...
The tensor network, as a facterization of tensors, aims at performing the operations that are common...
Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such...
In many applications, it is needed to change the topology of a tensor network directly and without a...
In many applications, it is needed to change the topology of a tensor network directly and without a...
Tensor networks are powerful factorization techniques which reduce resource requirements for numeric...
A tensor network is a type of decomposition used to express and approximate large arrays of data. A ...
Contracting tensor networks is often computationally demanding. Well-designed contraction sequences ...
We present a conceptually clear and algorithmically useful framework for parameterizing the costs of...
For each tensor network, the number of tensors (|V|), edges (|E|), and optimal contraction complexit...
Tensor network contraction is central to problems ranging from many-body physics to computer science...
The computational cost of contracting a tensor network depends on the sequence of contractions, but ...
The computational cost of contracting a tensor network depends on the sequence of contractions, but ...
We introduce a method for transforming low-order tensors into higher-order tensors and apply it to ...
We introduce a method for transforming low-order tensors into higher-order tensors and apply it to t...
textabstractWe introduce a method for transforming low-order tensors into higher-order tensors and ...
The tensor network, as a facterization of tensors, aims at performing the operations that are common...
Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such...
In many applications, it is needed to change the topology of a tensor network directly and without a...
In many applications, it is needed to change the topology of a tensor network directly and without a...
Tensor networks are powerful factorization techniques which reduce resource requirements for numeric...
A tensor network is a type of decomposition used to express and approximate large arrays of data. A ...