Enhance transform and plot docs

docs/refs.bib

@@ -95,4 +95,23 @@ @misc{cotengra
  howpublished={https://github.com/jcmgray/cotengra},
  url={https://github.com/jcmgray/cotengra},
 }
+@article{arute2019quantum,
+ title={Quantum supremacy using a programmable superconducting processor},
+ author={Arute, Frank and Arya, Kunal and Babbush, Ryan and Bacon, Dave and Bardin, Joseph C and Barends, Rami and Biswas, Rupak and Boixo, Sergio and Brandao, Fernando GSL and Buell, David A and others},
+ journal={Nature},
+ volume={574},
+ number={7779},
+ pages={505--510},
+ year={2019},
+ publisher={Nature Publishing Group}
+}
+@article{gray2021hyper,
+ title={Hyper-optimized tensor network contraction},
+ author={Gray, Johnnie and Kourtis, Stefanos},
+ journal={Quantum},
+ volume={5},
+ pages={410},
+ year={2021},
+ publisher={Verein zur F{\"o}rderung des Open Access Publizierens in den Quantenwissenschaften}
+}
 }

docs/src/transformations.md

@@ -4,7 +4,7 @@ In tensor network computations, it is good practice to apply various transformat
 
 A crucial reason why these methods are indispensable lies in their ability to drastically reduce the problem size of the contraction path search and also the contraction. This doesn't necessarily involve reducing the maximum rank of the Tensor Network itself, but more importantly, it reduces the size (or rank) of the involved tensors.
 
-Our approach has been significantly inspired by the ideas presented in the [Quimb](https://quimb.readthedocs.io/) library, explained in [this paper](https://arxiv.org/pdf/2002.01935.pdf).
+Our approach has been significantly inspired by the ideas presented in the [Quimb](https://quimb.readthedocs.io/) library, explained in [this paper](https://arxiv.org/pdf/2002.01935.pdf)[gray2021hyper](@cite).
 
 In Tenet, we provide a set of predefined transformations which you can apply to your `TensorNetwork` using both the `transform`/`transform!` functions.
 
@@ -249,7 +249,7 @@ fig #hide
 
 ## Example: RQC simplification
 
-Here we show how can we reduce the complexity of the tensor network by applying a tranformation to it. We take as an example the Sycamore circuit from the [Google's quantum supremacy paper](https://www.nature.com/articles/s41586-019-1666-5)
+Here we show how can we reduce the complexity of the tensor network by applying a tranformation to it. We take as an example the Sycamore circuit from the Google's quantum advantage paper[arute2019quantum](@cite).
 
 ```@setup plot
 using Makie
@@ -317,4 +317,4 @@ Label(fig[1, 1, Bottom()], "Original") # hide
 Label(fig[1, 2, Bottom()], "Transformed") # hide
 
 fig # hide
-```
+```

ext/TenetMakieExt.jl

@@ -19,6 +19,8 @@ Plot a [`TensorNetwork`](@ref) as a graph.
  - `inds` Whether to show the index labels. Defaults to `false`.
  - `layout` Algorithm used to map graph vertices to a (2D or 3D) coordinate system.
  The algorithms implemented in the `NetworkLayout` package are recommended.
+ - `labels` Whether to show the tensor labels. Defaults to `false`.
+ - Additionally, all the keyword arguments of `GraphMakie.graphplot` are supported.
 """
 function Makie.plot(tn::TensorNetwork; kwargs...)
  f = Figure()