(Top) A 1D binary MERA with a 16-site lattice and 3 levels of coarsening; three operator placements are highlighted (red, blue, green). (Bottom) Causal cones and final tensor networks for each of the three highlighted operators. Note that the tensor networks for the left-most (red) and right-most (green) operators are isomorphic to one another, but structurally distinct from the middle (blue) operator’s network.</p
In the last years, the classical simulation of quantum systems is growing as a good approach to prov...
For each tensor network, the number of tensors (|V|), edges (|E|), and optimal contraction complexit...
In panels (A-C) the x-axis labels the kind of network, with the curly braces grouping the mycelia (f...
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 ...
Tree tensor network (TTN) provides an essential theoretical framework for the practical simulation o...
Modern applications in engineering and data science are increasingly based on multidimensional data ...
(Center) For each lattice type (1D binary or 2D 4-ary) and number of operators, as the number of lev...
Contracting tensor networks is often computationally demanding. Well-designed contraction sequences ...
In this thesis, we develop an algebraic and graph theoretical reinterpretation of tensor networks an...
In this thesis, we develop an algebraic and graph theoretical reinterpretation of tensor networks an...
The number of edges (or wires) connecting to a tensor is equal to that tensor’s rank. When an index ...
Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2016, Tutor: ...
Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such...
<p><b>(A)</b> On the <b>left</b>, an example reconstruction of neural locations based on a simulatio...
In the last years, the classical simulation of quantum systems is growing as a good approach to prov...
For each tensor network, the number of tensors (|V|), edges (|E|), and optimal contraction complexit...
In panels (A-C) the x-axis labels the kind of network, with the curly braces grouping the mycelia (f...
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 ...
Tree tensor network (TTN) provides an essential theoretical framework for the practical simulation o...
Modern applications in engineering and data science are increasingly based on multidimensional data ...
(Center) For each lattice type (1D binary or 2D 4-ary) and number of operators, as the number of lev...
Contracting tensor networks is often computationally demanding. Well-designed contraction sequences ...
In this thesis, we develop an algebraic and graph theoretical reinterpretation of tensor networks an...
In this thesis, we develop an algebraic and graph theoretical reinterpretation of tensor networks an...
The number of edges (or wires) connecting to a tensor is equal to that tensor’s rank. When an index ...
Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2016, Tutor: ...
Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such...
<p><b>(A)</b> On the <b>left</b>, an example reconstruction of neural locations based on a simulatio...
In the last years, the classical simulation of quantum systems is growing as a good approach to prov...
For each tensor network, the number of tensors (|V|), edges (|E|), and optimal contraction complexit...
In panels (A-C) the x-axis labels the kind of network, with the curly braces grouping the mycelia (f...