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Use random_itensor and random_mps (#6)
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* Use `random_itensor` and `random_mps`

* Bump to v0.1.11
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@mtfishman
mtfishman authored May 17, 2024
1 parent d7f7976 commit 5c3603f
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2 changes: 1 addition & 1 deletion Project.toml
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@@ -1,7 +1,7 @@
name = "ITensorVisualizationBase"
uuid = "cd2553d2-8bef-4d93-8a38-c62f17d5ad23"
authors = ["Matthew Fishman <[email protected]> and contributors"]
version = "0.1.10"
version = "0.1.11"

[deps]
AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -17,9 +17,9 @@ j = Index(10, "j")
k = Index(40, "k")
l = Index(40, "l")
m = Index(40, "m")
A = randomITensor(i, j, k)
B = randomITensor(i, j, l, m)
C = randomITensor(k, l)
A = random_itensor(i, j, k)
B = random_itensor(i, j, l, m)
C = random_itensor(k, l)
ABC = @visualize A * B * C
```
This will execute the contraction and output
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12 changes: 12 additions & 0 deletions examples/Project.toml
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[deps]
GeometryBasics = "5c1252a2-5f33-56bf-86c9-59e7332b4326"
Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
ITensorMPS = "0d1a4710-d33b-49a5-8f18-73bdf49b47e2"
ITensorVisualizationBase = "cd2553d2-8bef-4d93-8a38-c62f17d5ad23"
ITensors = "9136182c-28ba-11e9-034c-db9fb085ebd5"
LayeredLayouts = "f4a74d36-062a-4d48-97cd-1356bad1de4e"
NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"

[compat]
ITensorMPS = "0.2.2"
ITensors = "0.6.8"
10 changes: 5 additions & 5 deletions examples/ex_dmrg.jl
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Expand Up @@ -14,11 +14,11 @@ x = Index([QN("Sz", 0) => 2]; tags="X")
y = Index([QN("Sz", 0) => 2]; tags="Y")

n = 2
ψn1n2 = randomITensor(l⃗[n - 1], s⃗[n], s⃗[n + 1], l⃗[n + 1], dag(x), dag(y))
hn1 = randomITensor(dag(h⃗[n - 1]), s⃗[n]', dag(s⃗[n]), h⃗[n], x, y)
hn2 = randomITensor(dag(h⃗[n]), s⃗[n + 1]', dag(s⃗[n + 1]), h⃗[n + 1])
ELn0 = randomITensor(l⃗[n - 1]', h⃗[n - 1], dag(l⃗[n - 1]))
ERn2 = randomITensor(l⃗[n + 1]', dag(h⃗[n + 1]), dag(l⃗[n + 1]))
ψn1n2 = random_itensor(l⃗[n - 1], s⃗[n], s⃗[n + 1], l⃗[n + 1], dag(x), dag(y))
hn1 = random_itensor(dag(h⃗[n - 1]), s⃗[n]', dag(s⃗[n]), h⃗[n], x, y)
hn2 = random_itensor(dag(h⃗[n]), s⃗[n + 1]', dag(s⃗[n + 1]), h⃗[n + 1])
ELn0 = random_itensor(l⃗[n - 1]', h⃗[n - 1], dag(l⃗[n - 1]))
ERn2 = random_itensor(l⃗[n + 1]', dag(h⃗[n + 1]), dag(l⃗[n + 1]))

edge_labels = (; plevs=true)

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2 changes: 1 addition & 1 deletion examples/ex_qn_mps.jl
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Expand Up @@ -3,7 +3,7 @@ using ITensorMPS
using ITensorVisualizationBase

s = siteinds("S=1/2", 5; conserve_qns=true)
ψ = randomMPS(s, n -> isodd(n) ? "↑" : "↓"; linkdims=2)
ψ = random_mps(s, n -> isodd(n) ? "↑" : "↓"; linkdims=2)
orthogonalize!(ψ, 2)
ψdag = prime(linkinds, dag(ψ))
tn = [ψ..., ψdag...]
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27 changes: 0 additions & 27 deletions examples/notest_ex_2d_circuit.jl
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23 changes: 0 additions & 23 deletions examples/notest_ex_qft_circuit.jl
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5 changes: 5 additions & 0 deletions test/Project.toml
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Expand Up @@ -2,7 +2,12 @@
GeometryBasics = "5c1252a2-5f33-56bf-86c9-59e7332b4326"
Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
ITensorMPS = "0d1a4710-d33b-49a5-8f18-73bdf49b47e2"
ITensorVisualizationBase = "cd2553d2-8bef-4d93-8a38-c62f17d5ad23"
ITensors = "9136182c-28ba-11e9-034c-db9fb085ebd5"
LayeredLayouts = "f4a74d36-062a-4d48-97cd-1356bad1de4e"
NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[compat]
ITensorMPS = "0.2.2"
ITensors = "0.6.8"
10 changes: 5 additions & 5 deletions test/test_basics.jl
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Expand Up @@ -16,11 +16,11 @@ using Test
y = Index([QN("Sz", 0) => 2]; tags="Y")

n = 2
ψn1n2 = randomITensor(l⃗[n - 1], s⃗[n], s⃗[n + 1], l⃗[n + 1], dag(x), dag(y))
hn1 = randomITensor(dag(h⃗[n - 1]), s⃗[n]', dag(s⃗[n]), h⃗[n], x, y)
hn2 = randomITensor(dag(h⃗[n]), s⃗[n + 1]', dag(s⃗[n + 1]), h⃗[n + 1])
ELn0 = randomITensor(l⃗[n - 1]', h⃗[n - 1], dag(l⃗[n - 1]))
ERn2 = randomITensor(l⃗[n + 1]', dag(h⃗[n + 1]), dag(l⃗[n + 1]))
ψn1n2 = random_itensor(l⃗[n - 1], s⃗[n], s⃗[n + 1], l⃗[n + 1], dag(x), dag(y))
hn1 = random_itensor(dag(h⃗[n - 1]), s⃗[n]', dag(s⃗[n]), h⃗[n], x, y)
hn2 = random_itensor(dag(h⃗[n]), s⃗[n + 1]', dag(s⃗[n + 1]), h⃗[n + 1])
ELn0 = random_itensor(l⃗[n - 1]', h⃗[n - 1], dag(l⃗[n - 1]))
ERn2 = random_itensor(l⃗[n + 1]', dag(h⃗[n + 1]), dag(l⃗[n + 1]))

tn = [ELn0, ψn1n2, hn1, hn2, ERn2]

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Registration pull request created: JuliaRegistries/General/107065

Tip: Release Notes

Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.

@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.1.11 -m "<description of version>" 5c3603f2300e3d262f15da96c01ee7a73903c324
git push origin v0.1.11

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