Merge developments in preparation for release v0.4

Project.toml

@@ -4,16 +4,17 @@ authors = ["Sergio Sánchez Ramírez <[email protected]>"]
 version = "0.3.0"
 
 [deps]
-Bijections = "e2ed5e7c-b2de-5872-ae92-c73ca462fb04"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 DeltaArrays = "10b0fc19-5ccc-4427-889b-d75dd6306188"
 EinExprs = "b1794770-133b-4de1-afb4-526377e9f4c5"
 GraphMakie = "1ecd5474-83a3-4783-bb4f-06765db800d2"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+ImmutableArrays = "667c17eb-ab9b-4487-935f-1c621bb82497"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Muscle = "21fe5c4b-a943-414d-bf3e-516f24900631"
 OMEinsum = "ebe7aa44-baf0-506c-a96f-8464559b3922"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 ValSplit = "0625e100-946b-11ec-09cd-6328dd093154"
 
@@ -22,26 +23,26 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
-Quac = "b9105292-1415-45cf-bff1-d6ccf71e6143"
 
 [extensions]
 TenetChainRulesCoreExt = "ChainRulesCore"
 TenetChainRulesTestUtilsExt = ["ChainRulesCore", "ChainRulesTestUtils"]
 TenetFiniteDifferencesExt = "FiniteDifferences"
 TenetMakieExt = "Makie"
-TenetQuacExt = "Quac"
 
 [compat]
-Bijections = "0.1"
 ChainRulesCore = "1.0"
 Combinatorics = "1.0"
 DeltaArrays = "0.1.1"
 EinExprs = "0.5.5"
 GraphMakie = "0.4,0.5"
 Graphs = "1.7"
+LinearAlgebra = "1.9"
 Makie = "0.18, 0.19"
 Muscle = "0.1"
 OMEinsum = "0.7"
-Quac = "0.2"
+Random = "1.9"
+SparseArrays = "1.9"
+UUIDs = "1.9"
 ValSplit = "0.1"
 julia = "1.9"

README.md

@@ -7,37 +7,32 @@
 [![Documentation: stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://bsc-quantic.github.io/Tenet.jl/)
 [![Documentation: dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://bsc-quantic.github.io/Tenet.jl/dev/)
 
+> [!IMPORTANT]
+> The code for quantum tensor networks has been moved to the new [`Qrochet`](https://github.com/bsc-quantic/Qrochet.jl) library.
+
 A Julia library for **Ten**sor **Net**works. `Tenet` can be executed both at local environments and on large supercomputers. Its goals are,
 
 - **Expressiveness** _Simple to use._ 👶
 - **Flexibility** _Extend it to your needs._ 🔧
 - **Performance** _Goes brr... fast._ 🏎️
 
-A video of its presentation at JuliaCon 2023 can be seen here:
+## Features
 
-[![Watch the video](https://img.youtube.com/vi/8BHGtm6FRMk/maxresdefault.jpg)](https://youtu.be/8BHGtm6FRMk)
+- Optimized Tensor Network contraction order, powered by [`EinExprs`](https://github.com/bsc-quantic/EinExprs.jl)
+- Tensor Network slicing/cuttings
+- Automatic Differentiation of TN contraction
+- Distributed contraction
+- Local Tensor Network transformations
+  - Hyperindex converter
+  - Rank simplification
+  - Diagonal reduction
+  - Anti-diagonal gauging
+  - Column reduction
+  - Split simplification
+- 2D & 3D visualization of large networks, powered by [`Makie`](https://github.com/MakieOrg/Makie.jl)
 
-## Features
+## Preview
+
+A video of its presentation at JuliaCon 2023 can be seen here:
 
-- [x] Optimized Tensor Network contraction, powered by [`EinExprs`](https://github.com/bsc-quantic/EinExprs.jl)
-- [x] Tensor Network slicing/cuttings
-- [x] Automatic Differentiation of TN contraction
-- [ ] Distributed contraction
-- [ ] Quantum Tensor Networks
-  - [x] Matrix Product States (MPS)
-  - [x] Matrix Product Operators (MPO)
-  - [ ] Tree Tensor Networks (TTN)
-  - [x] Projected Entangled Pair States (PEPS)
-  - [ ] Multiscale Entanglement Renormalization Ansatz (MERA)
-- [ ] Numerical Tensor Network algorithms
-  - [ ] Tensor Renormalization Group (TRG)
-  - [ ] Density Matrix Renormalization Group (DMRG)
-- [x] Local Tensor Network transformations
-  - [x] Hyperindex converter
-  - [x] Rank simplification
-  - [x] Diagonal reduction
-  - [x] Anti-diagonal gauging
-  - [x] Column reduction
-  - [x] Split simplification
-- [x] 2D & 3D visualization of large networks, powered by [`Makie`](https://github.com/MakieOrg/Makie.jl)
-- [x] Translation from quantum circuits, powered by [`Quac`](https://github.com/bsc-quantic/Quac.jl)
+[![Watch the video](https://img.youtube.com/vi/8BHGtm6FRMk/maxresdefault.jpg)](https://youtu.be/8BHGtm6FRMk)

docs/Project.toml

@@ -5,7 +5,6 @@ DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
 EinExprs = "b1794770-133b-4de1-afb4-526377e9f4c5"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"
-QuacIO = "0dfb065c-ad45-4a42-9a50-cb82c165eb0b"
 Tenet = "85d41934-b9cd-44e1-8730-56d86f15f3ec"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 

docs/make.jl

@@ -20,21 +20,10 @@ makedocs(
     pages = Any[
         "Home"=>"index.md",
         "Tensors"=>"tensors.md",
-        "Tensor Networks"=>[
-            "Introduction" => "tensor-network.md",
-            "Contraction" => "contraction.md",
-            "Transformations" => "transformations.md",
-            "Visualization" => "visualization.md",
-        ],
-        "Quantum Tensor Networks"=>[
-            "Introduction" => "quantum/index.md",
-            "Matrix Product States (MPS)" => "quantum/mps.md",
-            "Projected Entangled Pair States (PEPS)" => "quantum/peps.md",
-        ],
-        "Examples"=>[
-            "Google's Quantum Advantage experiment" => "examples/google-rqc.md",
-            "Automatic Differentiation on Tensor Network contraction" => "examples/ad-tn.md",
-        ],
+        "Tensor Networks"=>"tensor-network.md",
+        "Contraction"=>"contraction.md",
+        "Transformations"=>"transformations.md",
+        "Visualization"=>"visualization.md",
         "Alternatives"=>"alternatives.md",
         "References"=>"references.md",
     ],

docs/refs.bib

@@ -1,15 +1,4 @@
 {
-@inproceedings{stoudenmire-tnml,
- author = {Stoudenmire, Edwin and Schwab, David J},
- booktitle = {Advances in Neural Information Processing Systems},
- editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
- pages = {},
- publisher = {Curran Associates, Inc.},
- title = {Supervised Learning with Tensor Networks},
- url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/5314b9674c86e3f9d1ba25ef9bb32895-Paper.pdf},
- volume = {29},
- year = {2016}
-}
 @article{gray2018quimb,
   title={quimb: A python package for quantum information and many-body calculations},
   author={Gray, Johnnie},
@@ -44,16 +33,6 @@ @article{ramon2023tensorkrowch
   pages={arXiv--2306},
   year={2023}
 }
-@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},
@@ -63,47 +42,4 @@ @article{gray2021hyper
   year={2021},
   publisher={Verein zur F{\"o}rderung des Open Access Publizierens in den Quantenwissenschaften}
 }
-@article{boixo2018characterizing,
-  title={Characterizing quantum supremacy in near-term devices},
-  author={Boixo, Sergio and Isakov, Sergei V and Smelyanskiy, Vadim N and Babbush, Ryan and Ding, Nan and Jiang, Zhang and Bremner, Michael J and Martinis, John M and Neven, Hartmut},
-  journal={Nature Physics},
-  volume={14},
-  number={6},
-  pages={595--600},
-  year={2018},
-  publisher={Nature Publishing Group UK London}
-}
-@article{villalonga2020establishing,
-  title={Establishing the quantum supremacy frontier with a 281 pflop/s simulation},
-  author={Villalonga, Benjamin and Lyakh, Dmitry and Boixo, Sergio and Neven, Hartmut and Humble, Travis S and Biswas, Rupak and Rieffel, Eleanor G and Ho, Alan and Mandr{\`a}, Salvatore},
-  journal={Quantum Science and Technology},
-  volume={5},
-  number={3},
-  pages={034003},
-  year={2020},
-  publisher={IOP Publishing}
-}
-@article{markov2018quantum,
-  title={Quantum supremacy is both closer and farther than it appears},
-  author={Markov, Igor L and Fatima, Aneeqa and Isakov, Sergei V and Boixo, Sergio},
-  journal={arXiv preprint arXiv:1807.10749},
-  year={2018}
-}
-@misc{cotengra,
-  title={cotengra: Hyper optimized contraction trees for large tensor networks and einsums},
-  author={Gray, Johnnie},
-  year={2021},
-  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}
-}
 }

docs/src/contraction.md

@@ -3,7 +3,7 @@
 Contraction path optimization and execution is delegated to the [`EinExprs`](https://github.com/bsc-quantic/EinExprs) library. A `EinExpr` is a lower-level form of a Tensor Network, in which the contraction path has been laid out as a tree. It is similar to a symbolic expression (i.e. `Expr`) but in which every node represents an Einstein summation expression (aka `einsum`).
 
 ```@docs
-einexpr(::TensorNetwork)
-contract(::TensorNetwork)
+einexpr(::Tenet.AbstractTensorNetwork)
+contract(::Tenet.AbstractTensorNetwork)
 contract!
 ```