diff --git a/Project.toml b/Project.toml
index eb3179e2..85719e4b 100644
--- a/Project.toml
+++ b/Project.toml
@@ -23,14 +23,12 @@ 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]
 ChainRulesCore = "1.0"
@@ -42,6 +40,5 @@ Graphs = "1.7"
 Makie = "0.18, 0.19"
 Muscle = "0.1"
 OMEinsum = "0.7"
-Quac = "0.2"
 ValSplit = "0.1"
 julia = "1.9"
diff --git a/README.md b/README.md
index c46bac1d..f23d752f 100644
--- a/README.md
+++ b/README.md
@@ -22,15 +22,6 @@ A video of its presentation at JuliaCon 2023 can be seen here:
 - [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
@@ -39,4 +30,3 @@ A video of its presentation at JuliaCon 2023 can be seen here:
   - [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)
diff --git a/docs/Project.toml b/docs/Project.toml
index e4a848fd..02b785d2 100644
--- a/docs/Project.toml
+++ b/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"
 
diff --git a/docs/make.jl b/docs/make.jl
index b8dff0c7..89c2d0b1 100644
--- a/docs/make.jl
+++ b/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",
     ],
diff --git a/docs/src/examples/ad-tn.md b/docs/src/examples/ad-tn.md
deleted file mode 100644
index 638dd735..00000000
--- a/docs/src/examples/ad-tn.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Automatic Differentiation on Tensor Network contraction
-
-```@setup autodiff
-using CairoMakie
-```
-
-Tensor Networks have recently gained popularity for Machine Learning tasks.
-In this example, we show how to perform Automatic Differentiation on Tensor Network contraction to overlap the overlap between two [Matrix Product States (MPS)](@ref) with a smaller dimension.
-
-```@example autodiff
-using Tenet
-using Zygote
-using Random: seed! # hide
-
-rng = seed!(4) # hide
-
-ψ = rand(MPS{Open}, n = 4, p = 2, χ = 2)
-ϕ = rand(MPS{Open}, n = 4, p = 2, χ = 4)
-ψ = rand(rng, MPS{Open}, n = 4, p = 2, χ = 2) # hide
-ϕ = rand(rng, MPS{Open}, n = 4, p = 2, χ = 4) # hide
-
-tn = merge(ψ, ϕ')
-
-plot(tn) # hide
-```
-
-This problem is known as _MPS compression_.
-While there are better methods for this matter, this example excels for its simplicity and it can easily be modified for ML tasks.
-The loss function minimizes when the overlap between the two states ``\psi`` and ``\phi`` maximizes, constrained to normalized states.
-
-```math
-\begin{aligned}
-\min_\psi \quad & \left(\braket{\phi | \psi} - 1\right)^2 \\
-\textrm{s.t.} \quad & \lVert \psi \rVert^2 = \braket{\psi \mid \psi} = 1 \\
- & \lVert \phi \rVert^2 = \braket{\phi \mid \phi} = 1
-\end{aligned}
-```
-
-!!! warning "Implicit parameters"
-    Currently, calling `Zygote.gradient`/`Zygote.jacobian` on functions with explicit parameters doesn't interact well with `Tenet` data-structures (i.e. `Tensor` and `TensorNetwork`) on the interface.
-
-    While the problem persists, use implicit parameters with `Zygote.Params` on the arrays (i.e. call `Params([parent(tensor)])` or `Params([arrays(tensor_network)])`).
-
-```@example autodiff
-η = 0.01
-@time losses = map(1:200) do it
-    # compute gradient
-    loss, ∇ = withgradient(Params(arrays(ψ))) do
-        ((contract(tn) |> first) - 1)^2
-    end
-
-    # direct gradient descent
-    for array in arrays(ψ)
-        array .-= η * ∇[array]
-    end
-
-    # normalize state
-    normalize!(ψ)
-
-    return loss
-end
-
-f = Figure() # hide
-ax = Axis(f[1, 1], yscale = log10, xscale = identity, xlabel="Iterations") # hide
-lines!(losses, label="Loss") # hide
-lines!(map(x -> 1 - sqrt(x), losses), label="Overlap") # hide
-f[1,2] = Legend(f, ax, framevisible=false) # hide
-f # hide
-```
diff --git a/docs/src/examples/google-rqc.md b/docs/src/examples/google-rqc.md
deleted file mode 100644
index 8f945228..00000000
--- a/docs/src/examples/google-rqc.md
+++ /dev/null
@@ -1,93 +0,0 @@
-# Google's Quantum Advantage experiment
-
-```@setup circuit
-using CairoMakie
-CairoMakie.activate!(type = "svg")
-using NetworkLayout
-```
-
-!!! info "Dependencies 📦"
-    This example uses `QuacIO` and `EinExprs` in combination with `Tenet`.
-    Both packages can be found in [Quantic's registry](https://github.com/bsc-quantic/Registry) and can be installed in Pkg mode.
-
-    ```julia
-    add QuacIO EinExprs
-    ```
-
-    It also requires the circuit in `sycamore_m53_d10.qasm` file that can be found in [here](./sycamore_53_10_0.qasm).
-    This is a shorter version of the real circuit used for the experiment.
-
-In 2019, Google rushed to claim _quantum advantage_[^supremacy] for the first time ever [arute2019quantum](@cite)[villalonga2020establishing](@cite).
-The article was highly criticized and one year later, it was disproved [gray2021hyper](@cite) by developing a better heuristic search for contraction path which provided a $\times 10^4$ speedup.
-
-[^supremacy]: The first used term was _quantum supremacy_ although the community transitioned to _quantum advantage_ due to political reasons. However, Google now uses the term _beyond classical_. It is then not uncommon to find different terms to refer to the same thing: the moment in which quantum computers surpass classical computers on solving some problem.
-
-Since then, several teams and companies have come and go, proposing and disproving several experiments. But in this example, we focus on the original Google experiment.
-
-In short, the experiment consisted on sampling Random Quantum Circuits (RQC).
-The state of the systems after these circuits follow a distribution similar, but **not equal** to the uniform distribution.
-Due to noise and decoherence, the fidelity of quantum chips decrease with the circuit depth.
-The complexity of contracting tensor networks grows with the circuit depth, but due to the fidelity of the physical realization being small, a very rough approximation can be used.
-In the case of Google, they used _tensor slicing_ for projecting some expensive to contract indices.
-Since the contribution of each quantum path is guessed to be similar, each slice should contribute a similar part, and by taking the same percentage of slices as the fidelity of the quantum experiment, we obtain a result with a similar fidelity.
-If you want to read more on the topic, check out [boixo2018characterizing](@cite),[markov2018quantum](@cite).
-
-Thanks to `Tenet`'s much cared design, the experiment can be replicated conceptually in less than 20loc.
-
-```@example circuit
-using QuacIO
-using Tenet
-
-_sites = [5, 6, 14, 15, 16, 17, 24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 67, 72, 73, 74, 75, 76, 83, 84, 85, 94];
-
-# load circuit and convert to `TensorNetwork`
-circuit = QuacIO.parse(joinpath(@__DIR__, "sycamore_53_10_0.qasm"), format = QuacIO.Qflex(), sites = _sites);
-tn = QuantumTensorNetwork(circuit)
-tn = view(tn, [i => 1 for i in inds(tn, set=:open)]...)
-plot(tn) # hide
-```
-
-In order to aid the contraction path optimization, we shrink the search space by using local transformations.
-
-```@example circuit
-# simplify Tensor Network by preemptively contracting trivial cases
-tn = transform(tn, Tenet.RankSimplification)
-plot(tn, layout=Stress()) # hide
-```
-
-Contraction path optimization is the focus of the [`EinExprs`](https://github.com/bsc-quantic/EinExprs.jl) package. For this example, we will use the `Greedy` algorithm which doesn't yield the optimal path but it's fast and reproducible.
-
-```@example circuit
-using EinExprs
-path = einexpr(tn, optimizer = Greedy)
-plot(path, layout=Stress()) # hide
-```
-
-Then, the indices to be sliced have to be selected. `EinExprs` provides us with the `findslices` algorithm (based in the `SliceFinder` algorithm of [cotengra](@cite)) to suggest candidate indices for slicing.
-
-```julia
-cuttings = [[i => dim for dim in 1:size(tn,i)] for i in findslices(FlopsScorer(), path, slices=100)]
-```
-
-Finally, the contraction of slices is parallelized using distributed workers and each contribution is summed to `result`.
-
-```julia
-using Distributed
-using Iterators: product
-
-addprocs(10)
-
-@everywhere using Tenet, EinExprs
-@everywhere tn = $tn
-@everywhere path = $path
-
-result = @distributed (+) for proj_inds in product(cuttings...)
-    slice = view(tn, proj_inds...)
-
-    for indices in contractorder(path)
-        contract!(slice, indices)
-    end
-
-    tensors(slice) |> only
-end
-```
diff --git a/docs/src/examples/sycamore_53_10_0.qasm b/docs/src/examples/sycamore_53_10_0.qasm
deleted file mode 100644
index 1d701b02..00000000
--- a/docs/src/examples/sycamore_53_10_0.qasm
+++ /dev/null
@@ -1,1659 +0,0 @@
-53
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-29 rz(-0.9621689450218167) 56
-29 rz(-0.8955807139987333) 66
-29 rz(-0.7528365530460183) 63
-29 rz(0.7258226701533779) 73
-29 rz(-0.7596124788130051) 65
-29 rz(0.7153118414371823) 75
-29 rz(-0.6814566172688604) 74
-29 rz(0.7119632677360634) 84
-30 fsim(0.4836037489865321, 0.15720448517258814) 5 15
-30 fsim(0.4813027746287272, 0.16589400016587655) 14 24
-30 fsim(0.5141011050173628, 0.23139995996898027) 16 26
-30 fsim(0.4860333525890109, 0.1603093406600409) 25 35
-30 fsim(0.48318175023922383, 0.1564279262034107) 27 37
-30 fsim(0.4893783671604143, 0.1436061569230382) 32 42
-30 fsim(0.4885622789540625, 0.14820473190374475) 34 44
-30 fsim(0.48365869422848307, 0.16133729898079696) 36 46
-30 fsim(0.5035633150535174, 0.17812225242391694) 38 48
-30 fsim(0.47971143268846445, 0.14388623656266197) 41 51
-30 fsim(0.48615849941720374, 0.1566149748128868) 43 53
-30 fsim(0.4819082002062166, 0.14615816911657503) 45 55
-30 fsim(0.4909994945412715, 0.16098322131650447) 47 57
-30 fsim(0.4912337946314961, 0.18012457108936253) 52 62
-30 fsim(0.46508517467774446, 0.17301578991022867) 54 64
-30 fsim(0.4625029911035914, 0.13951781372243774) 56 66
-30 fsim(0.5169231411118692, 0.15867863143208272) 63 73
-30 fsim(0.4737902812504438, 0.15803377395648677) 65 75
-30 fsim(0.47857129167400153, 0.15930690639357745) 74 84
-31 rz(0.08867615219877248) 5
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-31 rz(-0.6633768584153672) 26
-31 rz(0.7278338235086499) 25
-31 rz(-0.2957685744316189) 35
-31 rz(-0.7470909692987385) 27
-31 rz(0.9386442253028005) 37
-31 rz(-0.534116953419378) 32
-31 rz(0.6147735989646066) 42
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-31 rz(-0.08936269675872473) 44
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-31 rz(0.24050825583717475) 38
-31 rz(-0.22243377643924597) 48
-31 rz(-0.968723820269696) 41
-31 rz(0.9634982992830347) 51
-31 rz(-0.5154066862373383) 43
-31 rz(0.04608053501176949) 53
-31 rz(-0.24375561287182076) 45
-31 rz(0.3465598421278222) 55
-31 rz(-0.6402034399153681) 47
-31 rz(0.608231384200875) 57
-31 rz(-0.4706912009416499) 52
-31 rz(0.4720669988255795) 62
-31 rz(-0.1947423470531135) 54
-31 rz(0.2026872397471489) 64
-31 rz(0.518418784954393) 56
-31 rz(-0.37616844397494287) 66
-31 rz(0.13212668683985648) 63
-31 rz(-0.15914056973249802) 73
-31 rz(0.698842568523934) 65
-31 rz(-0.7431432058997568) 75
-31 rz(0.3162523839911777) 74
-31 rz(-0.28574573352397464) 84
-32 hz_1_2 5
-32 x_1_2 6
-32 hz_1_2 14
-32 y_1_2 15
-32 x_1_2 16
-32 hz_1_2 17
-32 hz_1_2 24
-32 hz_1_2 25
-32 x_1_2 26
-32 x_1_2 27
-32 x_1_2 28
-32 y_1_2 32
-32 x_1_2 33
-32 x_1_2 34
-32 x_1_2 35
-32 y_1_2 36
-32 x_1_2 37
-32 hz_1_2 38
-32 y_1_2 39
-32 x_1_2 41
-32 x_1_2 42
-32 y_1_2 43
-32 hz_1_2 44
-32 hz_1_2 45
-32 hz_1_2 46
-32 x_1_2 47
-32 x_1_2 48
-32 hz_1_2 49
-32 x_1_2 50
-32 hz_1_2 51
-32 x_1_2 52
-32 hz_1_2 53
-32 x_1_2 54
-32 hz_1_2 55
-32 y_1_2 56
-32 y_1_2 57
-32 x_1_2 58
-32 x_1_2 61
-32 y_1_2 62
-32 hz_1_2 63
-32 y_1_2 64
-32 y_1_2 65
-32 x_1_2 66
-32 y_1_2 67
-32 hz_1_2 72
-32 x_1_2 73
-32 hz_1_2 74
-32 x_1_2 75
-32 hz_1_2 76
-32 hz_1_2 83
-32 y_1_2 84
-32 hz_1_2 85
-32 hz_1_2 94
-33 rz(-0.9936614516048078) 6
-33 rz(-0.9405204779283867) 16
-33 rz(0.37922888128394683) 15
-33 rz(-0.862623334213893) 25
-33 rz(-0.792215955848905) 17
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-33 rz(-0.6765196321192446) 24
-33 rz(0.7085773996725973) 34
-33 rz(-0.4948765210346792) 26
-33 rz(0.4753948502194507) 36
-33 rz(-0.40721920542516743) 28
-33 rz(0.45862221774016465) 38
-33 rz(-0.019367110142323812) 33
-33 rz(-0.11917392717195331) 43
-33 rz(-0.04776373634167188) 35
-33 rz(0.11966636432857744) 45
-33 rz(-0.6929732311767329) 37
-33 rz(0.7097429307239397) 47
-33 rz(-0.9847948791450413) 39
-33 rz(0.9915056046958216) 49
-33 rz(0.40547102672717017) 42
-33 rz(-0.40226676094889036) 52
-33 rz(0.14290045405943544) 44
-33 rz(-0.1896098315034204) 54
-33 rz(0.41690354904915433) 46
-33 rz(-0.23000431270863436) 56
-33 rz(-0.5270454946335372) 48
-33 rz(0.510943219324151) 58
-33 rz(0.48044877112063566) 51
-33 rz(-0.45783285276557767) 61
-33 rz(0.08044025457880374) 53
-33 rz(-0.48016819467688143) 63
-33 rz(0.9203905036170812) 55
-33 rz(-0.9961311900075136) 65
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-33 rz(-0.06798169568681166) 67
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-33 rz(-0.6284453761113048) 64
-33 rz(0.6275178045586413) 74
-33 rz(0.7803616255905353) 66
-33 rz(0.9384903862480122) 76
-33 rz(-0.5008590525212017) 73
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-33 rz(-0.5851407522809633) 85
-33 rz(0.10497060514307784) 84
-33 rz(-0.46370175379836065) 94
-34 fsim(0.48248590238931144, 0.17720711726780922) 6 16
-34 fsim(0.4831173807612162, 0.1575031878359891) 15 25
-34 fsim(0.5104652445279683, 0.1518018478533972) 17 27
-34 fsim(0.483109325847836, 0.16101107882693258) 24 34
-34 fsim(0.48552394043342284, 0.14819609246068247) 26 36
-34 fsim(0.5137841338574078, 0.15735440433706077) 28 38
-34 fsim(0.49264217592278786, 0.14098000393237992) 33 43
-34 fsim(0.48501709381128927, 0.14949793435121178) 35 45
-34 fsim(0.48656355637175525, 0.18260338861410977) 37 47
-34 fsim(0.49284428894552623, 0.15792655003519906) 39 49
-34 fsim(0.521298630708583, 0.21928323341492764) 42 52
-34 fsim(0.486998183067378, 0.15361268278864498) 44 54
-34 fsim(0.486390931075892, 0.1626489398497966) 46 56
-34 fsim(0.4813094673968524, 0.15327107428645925) 48 58
-34 fsim(0.47975471412767756, 0.16199668846067358) 51 61
-34 fsim(0.492110274286689, 0.1687807392184565) 53 63
-34 fsim(0.4827686976879951, 0.14378566187650293) 55 65
-34 fsim(0.46465889570960195, 0.13416717007279197) 57 67
-34 fsim(0.48881240027593537, 0.14984846721738163) 62 72
-34 fsim(0.4831873565264152, 0.16620074089526124) 64 74
-34 fsim(0.4764882949770173, 0.13770458644228914) 66 76
-34 fsim(0.48112498558227507, 0.15642764309600338) 73 83
-34 fsim(0.47456208123909566, 0.15553396824213445) 75 85
-34 fsim(0.5144705816268026, 0.1596097876378056) 84 94
-35 rz(-0.9938064157554087) 6
-35 rz(-0.9403755137777857) 16
-35 rz(-0.5702094041622093) 15
-35 rz(0.08681495123226317) 25
-35 rz(0.789154116322751) 17
-35 rz(-0.571842690735044) 27
-35 rz(-0.7949374234818247) 24
-35 rz(0.8269951910351775) 34
-35 rz(0.08019984017982425) 26
-35 rz(-0.09968151099505272) 36
-35 rz(0.8422404521812253) 28
-35 rz(-0.7908374398662281) 38
-35 rz(-0.6448238420430421) 33
-35 rz(0.506282804728765) 43
-35 rz(0.28254193899705515) 35
-35 rz(-0.21063931101014957) 45
-35 rz(-0.28718992515393654) 37
-35 rz(0.30395962470114324) 47
-35 rz(0.7429645187565517) 39
-35 rz(-0.7362537932057713) 49
-35 rz(0.3828114015988804) 42
-35 rz(-0.3796071358206017) 52
-35 rz(-0.043697966677180636) 44
-35 rz(-0.0030114107668043153) 54
-35 rz(-0.25671338148420475) 46
-35 rz(0.4436126178247247) 56
-35 rz(0.7739898074110851) 48
-35 rz(-0.7900920827204714) 58
-35 rz(0.7919221690167495) 51
-35 rz(-0.7693062506616914) 61
-35 rz(-0.919492613449433) 53
-35 rz(0.5197646733513552) 63
-35 rz(0.9628947512695266) 55
-35 rz(0.9613645623400409) 65
-35 rz(-0.8278265769955084) 57
-35 rz(0.889021852193614) 67
-35 rz(-0.9825292244032231) 62
-35 rz(0.9744582908202193) 72
-35 rz(0.8968636250628911) 64
-35 rz(-0.8977911966155546) 74
-35 rz(0.5203942238917367) 66
-35 rz(-0.801542212053188) 76
-35 rz(0.2581315227573) 73
-35 rz(-0.30010229395653043) 83
-35 rz(-0.6971455144841298) 75
-35 rz(0.6904955282537633) 85
-35 rz(0.6214649565591956) 84
-35 rz(-0.9801961052144784) 94
-36 x_1_2 5
-36 y_1_2 6
-36 x_1_2 14
-36 x_1_2 15
-36 y_1_2 16
-36 y_1_2 17
-36 x_1_2 24
-36 x_1_2 25
-36 y_1_2 26
-36 hz_1_2 27
-36 y_1_2 28
-36 hz_1_2 32
-36 y_1_2 33
-36 y_1_2 34
-36 y_1_2 35
-36 x_1_2 36
-36 hz_1_2 37
-36 x_1_2 38
-36 hz_1_2 39
-36 hz_1_2 41
-36 hz_1_2 42
-36 x_1_2 43
-36 y_1_2 44
-36 y_1_2 45
-36 x_1_2 46
-36 y_1_2 47
-36 y_1_2 48
-36 x_1_2 49
-36 y_1_2 50
-36 y_1_2 51
-36 hz_1_2 52
-36 x_1_2 53
-36 hz_1_2 54
-36 y_1_2 55
-36 hz_1_2 56
-36 x_1_2 57
-36 y_1_2 58
-36 hz_1_2 61
-36 hz_1_2 62
-36 x_1_2 63
-36 hz_1_2 64
-36 x_1_2 65
-36 hz_1_2 66
-36 hz_1_2 67
-36 x_1_2 72
-36 hz_1_2 73
-36 x_1_2 74
-36 y_1_2 75
-36 y_1_2 76
-36 y_1_2 83
-36 hz_1_2 84
-36 y_1_2 85
-36 y_1_2 94
-37 rz(-0.5757835902011161) 5
-37 rz(0.8253436560816667) 15
-37 rz(-0.5646427083721929) 14
-37 rz(0.5656735882521988) 24
-37 rz(0.10070735765308102) 16
-37 rz(-0.10041470925350265) 26
-37 rz(-0.1299632423448818) 25
-37 rz(0.5620284914219106) 35
-37 rz(0.15632646931043123) 27
-37 rz(0.03522678669363078) 37
-37 rz(-0.6288434907069298) 32
-37 rz(0.7095001362521582) 42
-37 rz(-0.732096529512105) 34
-37 rz(0.7005221256191941) 44
-37 rz(0.13445594284484547) 36
-37 rz(-0.09096023527356352) 46
-37 rz(0.5716926792305023) 38
-37 rz(-0.5536181998325734) 48
-37 rz(0.9516084230940176) 41
-37 rz(-0.9568339440806789) 51
-37 rz(-0.27279565669992845) 43
-37 rz(-0.1965304945256404) 53
-37 rz(-0.3937343640361464) 45
-37 rz(0.4965385932921524) 55
-37 rz(0.2382369735159425) 47
-37 rz(-0.2702090292304356) 57
-37 rz(0.905384021176354) 52
-37 rz(-0.9040082232924209) 62
-37 rz(0.7978845245057673) 54
-37 rz(-0.7899396318117285) 64
-37 rz(-0.964168945021872) 56
-37 rz(-0.893580713998678) 66
-37 rz(0.435163446953961) 63
-37 rz(-0.46217732984660476) 73
-37 rz(0.4283875211869742) 65
-37 rz(-0.4726881585628004) 75
-37 rz(0.7965433827311167) 74
-37 rz(-0.7660367322639136) 84
-38 fsim(0.4836037489865321, 0.15720448517258814) 5 15
-38 fsim(0.4813027746287272, 0.16589400016587655) 14 24
-38 fsim(0.5141011050173628, 0.23139995996898027) 16 26
-38 fsim(0.4860333525890109, 0.1603093406600409) 25 35
-38 fsim(0.48318175023922383, 0.1564279262034107) 27 37
-38 fsim(0.4893783671604143, 0.1436061569230382) 32 42
-38 fsim(0.4885622789540625, 0.14820473190374475) 34 44
-38 fsim(0.48365869422848307, 0.16133729898079696) 36 46
-38 fsim(0.5035633150535174, 0.17812225242391694) 38 48
-38 fsim(0.47971143268846445, 0.14388623656266197) 41 51
-38 fsim(0.48615849941720374, 0.1566149748128868) 43 53
-38 fsim(0.4819082002062166, 0.14615816911657503) 45 55
-38 fsim(0.4909994945412715, 0.16098322131650447) 47 57
-38 fsim(0.4912337946314961, 0.18012457108936253) 52 62
-38 fsim(0.46508517467774446, 0.17301578991022867) 54 64
-38 fsim(0.4625029911035914, 0.13951781372243774) 56 66
-38 fsim(0.5169231411118692, 0.15867863143208272) 63 73
-38 fsim(0.4737902812504438, 0.15803377395648677) 65 75
-38 fsim(0.47857129167400153, 0.15930690639357745) 74 84
-39 rz(0.7506761521987528) 5
-39 rz(-0.5011160863182021) 15
-39 rz(0.37839485720929306) 14
-39 rz(-0.3773639773292872) 24
-39 rz(-0.8943304931850955) 16
-39 rz(0.894623141584675) 26
-39 rz(0.9478338235086012) 25
-39 rz(-0.5157685744315735) 35
-39 rz(-0.005090969298765407) 27
-39 rz(0.19664422530282744) 37
-39 rz(0.7998830465805966) 32
-39 rz(-0.7192264010353682) 42
-39 rz(0.9477882928658377) 34
-39 rz(-0.9793626967587485) 44
-39 rz(-0.359947677393651) 36
-39 rz(0.403443384964933) 46
-39 rz(0.5385082558371981) 38
-39 rz(-0.5204337764392692) 48
-39 rz(0.9552761797302897) 41
-39 rz(-0.9605017007169508) 51
-39 rz(0.0005933137626453817) 43
-39 rz(-0.46991946498821424) 53
-39 rz(-0.8397556128717996) 45
-39 rz(0.9425598421278055) 55
-39 rz(0.17179656008465377) 47
-39 rz(-0.20376861579914693) 57
-39 rz(0.495308799058324) 52
-39 rz(-0.49393300117439093) 62
-39 rz(-0.858742347053148) 54
-39 rz(0.8666872397471868) 64
-39 rz(0.5204187849544483) 56
-39 rz(-0.37816844397499816) 66
-39 rz(0.9441266868398739) 63
-39 rz(-0.9711405697325176) 73
-39 rz(-0.4891574314760486) 65
-39 rz(0.44485679410022244) 75
-39 rz(0.8382523839912006) 74
-39 rz(-0.8077457335239976) 84
-40 hz_1_2 5
-40 x_1_2 6
-40 hz_1_2 14
-40 hz_1_2 15
-40 x_1_2 16
-40 x_1_2 17
-40 hz_1_2 24
-40 hz_1_2 25
-40 x_1_2 26
-40 y_1_2 27
-40 x_1_2 28
-40 x_1_2 32
-40 hz_1_2 33
-40 x_1_2 34
-40 x_1_2 35
-40 hz_1_2 36
-40 x_1_2 37
-40 hz_1_2 38
-40 x_1_2 39
-40 x_1_2 41
-40 x_1_2 42
-40 hz_1_2 43
-40 x_1_2 44
-40 x_1_2 45
-40 hz_1_2 46
-40 hz_1_2 47
-40 x_1_2 48
-40 hz_1_2 49
-40 x_1_2 50
-40 x_1_2 51
-40 y_1_2 52
-40 y_1_2 53
-40 y_1_2 54
-40 x_1_2 55
-40 x_1_2 56
-40 y_1_2 57
-40 x_1_2 58
-40 y_1_2 61
-40 y_1_2 62
-40 hz_1_2 63
-40 y_1_2 64
-40 y_1_2 65
-40 x_1_2 66
-40 y_1_2 67
-40 hz_1_2 72
-40 y_1_2 73
-40 hz_1_2 74
-40 x_1_2 75
-40 hz_1_2 76
-40 x_1_2 83
-40 x_1_2 84
-40 hz_1_2 85
-40 x_1_2 94
diff --git a/docs/src/index.md b/docs/src/index.md
index a34d9ee4..3d7960ed 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -28,21 +28,4 @@ A video of its presentation at JuliaCon 2023 can be seen here:
 - Optimized Tensor Network contraction, powered by [`EinExprs`](https://github.com/bsc-quantic/EinExprs.jl)
 - Tensor Network slicing/cuttings
 - Automatic Differentiation of TN contraction, powered by [`EinExprs`](https://github.com/bsc-quantic/EinExprs.jl) and [`ChainRules`](https://github.com/JuliaDiff/ChainRulesCore.jl)
-- Quantum Tensor Networks
-  - Matrix Product States (MPS)
-  - Matrix Product Operators (MPO)
-  - Projected Entangled Pair States (PEPS)
 - 3D visualization of large networks, powered by [`Makie`](https://github.com/MakieOrg/Makie.jl)
-- Translation from quantum circuits, powered by [`Quac`](https://github.com/bsc-quantic/Quac.jl)
-
-### Roadmap
-
-The following feature are not yet implemented but are work in progress or are thought to be implemented in the near-mid future:
-
-- Distributed contraction
-- Quantum Tensor Networks
-  - Tree Tensor Networks (TTN)
-  - Multiscale Entanglement Renormalization Ansatz (MERA)
-- Numerical Tensor Network algorithms
-  - Tensor Renormalization Group (TRG)
-  - Density Matrix Renormalization Group (DMRG)
diff --git a/docs/src/quantum/index.md b/docs/src/quantum/index.md
deleted file mode 100644
index e8924934..00000000
--- a/docs/src/quantum/index.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Introduction
-
-In `Tenet`, we define a [`QuantumTensorNetwork`](@ref) as a [`TensorNetwork`](@ref) with a notion of sites and directionality.
-
-```@docs
-QuantumTensorNetwork
-plug
-sites
-```
-
-## Adjoint
-
-```@docs
-adjoint
-```
-
-## Norm
-
-```@docs
-LinearAlgebra.norm(::Tenet.AbstractQuantumTensorNetwork, ::Real)
-LinearAlgebra.normalize!(::Tenet.AbstractQuantumTensorNetwork, ::Real)
-```
-
-## Trace
-
-```@docs
-LinearAlgebra.tr(::Tenet.AbstractQuantumTensorNetwork)
-```
-
-## Fidelity
-
-```@docs
-fidelity
-```
diff --git a/docs/src/quantum/mps.md b/docs/src/quantum/mps.md
deleted file mode 100644
index 37d32cfd..00000000
--- a/docs/src/quantum/mps.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Matrix Product States (MPS)
-
-Matrix Product States (MPS) are a Quantum Tensor Network ansatz whose tensors are laid out in a 1D chain.
-Due to this, these networks are also known as _Tensor Trains_ in other mathematical fields.
-Depending on the boundary conditions, the chains can be open or closed (i.e. periodic boundary conditions).
-
-```@setup viz
-using Makie
-Makie.inline!(true)
-set_theme!(resolution=(800,200))
-
-using CairoMakie
-
-using Tenet
-using NetworkLayout
-```
-
-```@example viz
-fig = Figure() # hide
-
-tn_open = rand(MatrixProduct{State,Open}, n=10, χ=4) # hide
-tn_periodic = rand(MatrixProduct{State,Periodic}, n=10, χ=4) # hide
-
-plot!(fig[1,1], tn_open, layout=Spring(iterations=1000, C=0.5, seed=100)) # hide
-plot!(fig[1,2], tn_periodic, layout=Spring(iterations=1000, C=0.5, seed=100)) # hide
-
-Label(fig[1,1, Bottom()], "Open") # hide
-Label(fig[1,2, Bottom()], "Periodic") # hide
-
-fig # hide
-```
-
-## Matrix Product Operators (MPO)
-
-Matrix Product Operators (MPO) are the operator version of [Matrix Product State (MPS)](#matrix-product-states-mps).
-The major difference between them is that MPOs have 2 indices per site (1 input and 1 output) while MPSs only have 1 index per site (i.e. an output).
-
-```@example viz
-fig = Figure() # hide
-
-tn_open = rand(MatrixProduct{Operator,Open}, n=10, χ=4) # hide
-tn_periodic = rand(MatrixProduct{Operator,Periodic}, n=10, χ=4) # hide
-
-plot!(fig[1,1], tn_open, layout=Spring(iterations=1000, C=0.5, seed=100)) # hide
-plot!(fig[1,2], tn_periodic, layout=Spring(iterations=1000, C=0.5, seed=100)) # hide
-
-Label(fig[1,1, Bottom()], "Open") # hide
-Label(fig[1,2, Bottom()], "Periodic") # hide
-
-fig # hide
-```
-
-In `Tenet`, the generic `MatrixProduct` ansatz implements this topology. Type variables are used to address their functionality (`State` or `Operator`) and their boundary conditions (`Open` or `Periodic`).
-
-```@docs
-MatrixProduct
-MatrixProduct(::Any)
-```
diff --git a/docs/src/quantum/peps.md b/docs/src/quantum/peps.md
deleted file mode 100644
index 11a792ef..00000000
--- a/docs/src/quantum/peps.md
+++ /dev/null
@@ -1,52 +0,0 @@
-# Projected Entangled Pair States (PEPS)
-
-Projected Entangled Pair States (PEPS) are a Quantum Tensor Network ansatz whose tensors are laid out in a 2D lattice. Depending on the boundary conditions, the chains can be open or closed (i.e. periodic boundary conditions).
-
-```@setup viz
-using Makie
-Makie.inline!(true)
-set_theme!(resolution=(800,400))
-
-using CairoMakie
-CairoMakie.activate!(type = "svg")
-
-using Tenet
-using NetworkLayout
-```
-
-```@example viz
-fig = Figure() # hide
-
-tn_open = rand(PEPS{Open}, rows=10, cols=10, χ=4) # hide
-tn_periodic = rand(PEPS{Periodic}, rows=10, cols=10, χ=4) # hide
-
-plot!(fig[1,1], tn_open, layout=Stress(seed=1)) # hide
-plot!(fig[1,2], tn_periodic, layout=Stress(seed=10,dim=2,iterations=100000)) # hide
-
-Label(fig[1,1, Bottom()], "Open") # hide
-Label(fig[1,2, Bottom()], "Periodic") # hide
-
-fig # hide
-```
-
-## Projected Entangled Pair Operators (PEPO)
-
-```@example viz
-fig = Figure() # hide
-
-tn_open = rand(PEPO{Open}, rows=10, cols=10, χ=4) # hide
-tn_periodic = rand(PEPO{Periodic}, rows=10, cols=10, χ=4) # hide
-
-plot!(fig[1,1], tn_open, layout=Stress(seed=1)) # hide
-plot!(fig[1,2], tn_periodic, layout=Stress(seed=10,dim=2,iterations=100000)) # hide
-
-Label(fig[1,1, Bottom()], "Open") # hide
-Label(fig[1,2, Bottom()], "Periodic") # hide
-
-fig # hide
-```
-
-```@docs
-ProjectedEntangledPair
-ProjectedEntangledPair(::Any)
-```
diff --git a/docs/src/transformations.md b/docs/src/transformations.md
index ac482326..85b3eede 100644
--- a/docs/src/transformations.md
+++ b/docs/src/transformations.md
@@ -283,35 +283,3 @@ plot!(fig[1, 2], reduced, layout=Spring(iterations=10000, C=13, seed=151); node_
 
 fig #hide
 ```
-
-## Example: RQC simplification
-
-Local transformations can dramatically reduce the complexity of tensor networks. Take as an example the Random Quantum Circuit circuit on the Sycamore chip from Google's quantum advantage experiment [arute2019quantum](@cite).
-
-```@example plot
-using QuacIO
-set_theme!(resolution=(800,400)) # hide
-
-sites = [5, 6, 14, 15, 16, 17, 24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 67, 72, 73, 74, 75, 76, 83, 84, 85, 94]
-circuit = QuacIO.parse(joinpath(@__DIR__, "sycamore_53_10_0.qasm"), format=QuacIO.Qflex(), sites=sites)
-tn = QuantumTensorNetwork(circuit)
-
-# Apply transformations to the tensor network
-transformed_tn = transform(tn, [Tenet.AntiDiagonalGauging, Tenet.DiagonalReduction, Tenet.ColumnReduction, Tenet.RankSimplification])
-
-fig = Figure() # hide
-ax1 = Axis(fig[1, 1]) # hide
-p1 = plot!(ax1, tn; edge_width=0.75, node_size=8., node_attr=(strokecolor=:black, strokewidth=0.5)) # hide
-ax2 = Axis(fig[1, 2]) # hide
-p2 = plot!(ax2, transformed_tn; edge_width=0.75, node_size=8., node_attr=(strokecolor=:black, strokewidth=0.5)) # hide
-ax1.titlesize, ax2.titlesize = 20, 20 # hide
-hidedecorations!(ax1) # hide
-hidespines!(ax1) # hide
-hidedecorations!(ax2) # hide
-hidespines!(ax2) # hide
-
-Label(fig[1, 1, Bottom()], "Original") # hide
-Label(fig[1, 2, Bottom()], "Transformed") # hide
-
-fig # hide
-```
diff --git a/ext/TenetQuacExt.jl b/ext/TenetQuacExt.jl
deleted file mode 100644
index 3879fdbf..00000000
--- a/ext/TenetQuacExt.jl
+++ /dev/null
@@ -1,40 +0,0 @@
-module TenetQuacExt
-
-using Tenet
-using Quac: Circuit, lanes, arraytype, Swap
-
-function Tenet.QuantumTensorNetwork(circuit::Circuit)
-    n = lanes(circuit)
-    wire = [[Tenet.letter(i)] for i in 1:n]
-    tn = TensorNetwork()
-
-    i = n + 1
-
-    for gate in circuit
-        G = arraytype(gate)
-        array = G(gate)
-
-        if gate isa Swap
-            (a, b) = lanes(gate)
-            wire[a], wire[b] = wire[b], wire[a]
-            continue
-        end
-
-        inds = map(lanes(gate)) do l
-            from, to = last(wire[l]), Tenet.letter(i)
-            i += 1
-            push!(wire[l], to)
-            (from, to)
-        end |> x -> zip(x...) |> Iterators.flatten |> collect
-
-        tensor = Tensor(array, inds)
-        push!(tn, tensor)
-    end
-
-    input = first.(wire)
-    output = last.(wire)
-
-    return QuantumTensorNetwork(tn, input, output)
-end
-
-end
diff --git a/src/Quantum/MP.jl b/src/Quantum/MP.jl
deleted file mode 100644
index 18ba008a..00000000
--- a/src/Quantum/MP.jl
+++ /dev/null
@@ -1,192 +0,0 @@
-using UUIDs: uuid4
-using Base.Iterators: flatten
-using Random
-using Muscle: gramschmidt!
-using EinExprs: inds
-using Classes
-
-"""
-    MatrixProduct{P<:Plug,B<:Boundary} <: Ansatz
-
-A generic ansatz representing Matrix Product State (MPS) and Matrix Product Operator (MPO) topology, aka Tensor Train.
-Type variable `P` represents the `Plug` type (`State` or `Operator`) and `B` represents the `Boundary` type (`Open` or `Periodic`).
-"""
-struct MatrixProduct{P<:Plug,B<:Boundary} <: Ansatz end
-
-function MatrixProduct{P}(arrays; boundary::Type{<:Boundary} = Open, kwargs...) where {P<:Plug}
-    MatrixProduct{P,boundary}(arrays; kwargs...)
-end
-
-const MPS = MatrixProduct{State}
-const MPO = MatrixProduct{Operator}
-
-plug(::Type{<:MatrixProduct{P}}) where {P} = P()
-boundary(::Type{<:MatrixProduct{P,B}}) where {P,B} = B()
-
-sitealias(::Type{MatrixProduct{P,Open}}, order, n, i) where {P<:Plug} =
-    if i == 1
-        filter(!=(:l), order)
-    elseif i == n
-        filter(!=(:r), order)
-    else
-        order
-    end
-sitealias(::Type{MatrixProduct{P,Periodic}}, order, n, i) where {P<:Plug} = tuple(order...)
-
-defaultorder(::Type{<:MatrixProduct{Property}}) = (:l, :r)
-defaultorder(::Type{<:MatrixProduct{State}}) = (:l, :r, :o)
-defaultorder(::Type{<:MatrixProduct{Operator}}) = (:l, :r, :i, :o)
-
-"""
-    MatrixProduct{P,B}(arrays::AbstractArray[]; order = defaultorder(MatrixProduct{P}))
-
-Construct a [`TensorNetwork`](@ref) with [`MatrixProduct`](@ref) ansatz, from the arrays of the tensors.
-
-# Keyword Arguments
-
-  - `order` Order of tensor indices on `arrays`. Defaults to `(:l, :r, :o)` if `P` is a `State`, `(:l, :r, :i, :o)` if `Operator`.
-"""
-function MatrixProduct{P,B}(arrays; order = defaultorder(MatrixProduct{P})) where {P<:Plug,B<:Boundary}
-    issetequal(order, defaultorder(MatrixProduct{P})) || throw(
-        ArgumentError(
-            "`order` must be a permutation of $(join(String.(defaultorder(MatrixProduct{P})), ',', " and "))",
-        ),
-    )
-
-    n = length(arrays)
-    vinds = Dict(x => Symbol(uuid4()) for x in ringpeek(1:n))
-    oinds = map(_ -> Symbol(uuid4()), 1:n)
-    iinds = map(_ -> Symbol(uuid4()), 1:n)
-
-    input, output = if P <: Property
-        Symbol[], Symbol[]
-    elseif P <: State
-        Symbol[], oinds
-    elseif P <: Operator
-        iinds, oinds
-    else
-        throw(ArgumentError("Plug $P is not valid"))
-    end
-
-    tensors::Vector{Tensor} = map(enumerate(arrays)) do (i, array)
-        dirs = sitealias(MatrixProduct{P,B}, order, n, i)
-
-        inds = map(dirs) do dir
-            if dir === :l
-                vinds[(mod1(i - 1, n), i)]
-            elseif dir === :r
-                vinds[(i, mod1(i + 1, n))]
-            elseif dir === :o
-                oinds[i]
-            elseif dir === :i
-                iinds[i]
-            end
-        end
-
-        Tensor(array, inds)
-    end
-
-    return QuantumTensorNetwork(TensorNetwork(tensors), input, output)
-end
-
-# NOTE does not use optimal contraction path, but "parallel-optimal" which costs x2 more
-# function contractpath(a::TensorNetwork{<:MatrixProductState}, b::TensorNetwork{<:MatrixProductState})
-#     !issetequal(sites(a), sites(b)) && throw(ArgumentError("both tensor networks are expected to have same sites"))
-
-#     b = replace(b, [nameof(outsiteind(b, s)) => nameof(outsiteind(a, s)) for s in sites(a)]...)
-#     path = nameof.(flatten([physicalinds(a), flatten(zip(virtualinds(a), virtualinds(b)))]) |> collect)
-#     inputs = flatten([tensors(a), tensors(b)]) .|> inds
-#     output = Symbol[]
-#     size_dict = merge(size(a), size(b))
-
-#     ContractionPath(path, inputs, output, size_dict)
-# end
-
-# TODO let choose the orthogonality center
-function Base.rand(rng::Random.AbstractRNG, sampler::QTNSampler{MatrixProduct{State,Open}})
-    n = sampler.n
-    χ = sampler.χ
-    p = get(sampler, :p, 2)
-    T = get(sampler, :eltype, Float64)
-
-    arrays::Vector{AbstractArray{T}} = map(1:n) do i
-        χl, χr = let after_mid = i > n ÷ 2, i = (n + 1 - abs(2i - n - 1)) ÷ 2
-            χl = min(χ, p^(i - 1))
-            χr = min(χ, p^i)
-
-            # swap bond dims after mid and handle midpoint for odd-length MPS
-            (isodd(n) && i == n ÷ 2 + 1) ? (χl, χl) : (after_mid ? (χr, χl) : (χl, χr))
-        end
-
-        # fix for first site
-        i == 1 && ((χl, χr) = (χr, 1))
-
-        # orthogonalize by Gram-Schmidt algorithm
-        A = gramschmidt!(rand(rng, T, χl, χr * p))
-
-        reshape(A, χl, χr, p)
-    end
-
-    # reshape boundary sites
-    arrays[1] = reshape(arrays[1], p, p)
-    arrays[n] = reshape(arrays[n], p, p)
-
-    # normalize state
-    arrays[1] ./= sqrt(p)
-
-    MatrixProduct{State,Open}(arrays)
-end
-
-# TODO let choose the orthogonality center
-# TODO different input/output physical dims
-function Base.rand(rng::Random.AbstractRNG, sampler::QTNSampler{MatrixProduct{Operator,Open}})
-    n = sampler.n
-    χ = sampler.χ
-    p = get(sampler, :p, 2)
-    T = get(sampler, :eltype, Float64)
-
-    ip = op = p
-
-    arrays::Vector{AbstractArray{T}} = map(1:n) do i
-        χl, χr = let after_mid = i > n ÷ 2, i = (n + 1 - abs(2i - n - 1)) ÷ 2
-            χl = min(χ, ip^(i - 1) * op^(i - 1))
-            χr = min(χ, ip^i * op^i)
-
-            # swap bond dims after mid and handle midpoint for odd-length MPS
-            (isodd(n) && i == n ÷ 2 + 1) ? (χl, χl) : (after_mid ? (χr, χl) : (χl, χr))
-        end
-
-        shape = if i == 1
-            (χr, ip, op)
-        elseif i == n
-            (χl, ip, op)
-        else
-            (χl, χr, ip, op)
-        end
-
-        # orthogonalize by Gram-Schmidt algorithm
-        A = gramschmidt!(rand(rng, T, shape[1], prod(shape[2:end])))
-
-        reshape(A, shape)
-    end
-
-    # normalize
-    ζ = min(χ, ip * op)
-    arrays[1] ./= sqrt(ζ)
-
-    MatrixProduct{Operator,Open}(arrays)
-end
-
-# TODO stable renormalization
-# TODO different input/output physical dims for Operator
-function Base.rand(rng::Random.AbstractRNG, sampler::QTNSampler{MatrixProduct{P,Periodic}}) where {P<:Plug}
-    n = sampler.n
-    χ = sampler.χ
-    p = get(sampler, :p, 2)
-    T = get(sampler, :eltype, Float64)
-
-    A = MatrixProduct{P,Periodic}([rand(rng, T, [P === State ? (χ, χ, p) : (χ, χ, p, p)]...) for _ in 1:n])
-    normalize!(A)
-
-    return A
-end
diff --git a/src/Quantum/PEP.jl b/src/Quantum/PEP.jl
deleted file mode 100644
index 06e36d74..00000000
--- a/src/Quantum/PEP.jl
+++ /dev/null
@@ -1,173 +0,0 @@
-using UUIDs: uuid4
-using Classes
-
-"""
-    ProjectedEntangledPair{P<:Plug,B<:Boundary} <: Ansatz
-
-A generic ansatz representing Projected Entangled Pair States (PEPS) and Projected Entangled Pair Operators (PEPO).
-Type variable `P` represents the `Plug` type (`State` or `Operator`) and `B` represents the `Boundary` type (`Open` or `Periodic`).
-"""
-struct ProjectedEntangledPair{P<:Plug,B<:Boundary} <: Ansatz end
-
-function ProjectedEntangledPair{P}(arrays; boundary::Type{<:Boundary} = Open, kwargs...) where {P<:Plug}
-    ProjectedEntangledPair{P,boundary}(arrays; kwargs...)
-end
-
-const PEPS = ProjectedEntangledPair{State}
-const PEPO = ProjectedEntangledPair{Operator}
-
-plug(::Type{<:ProjectedEntangledPair{P}}) where {P} = P()
-boundary(::Type{<:ProjectedEntangledPair{P,B}}) where {P,B} = B()
-
-function sitealias(::Type{<:ProjectedEntangledPair{P,Open}}, order, size, pos) where {P<:Plug}
-    m, n = size
-    i, j = pos
-
-    order = [order...]
-
-    filter(order) do dir
-        !(i == 1 && dir === :u || i == m && dir === :d || j == 1 && dir === :l || j == n && dir === :r)
-    end
-end
-sitealias(::Type{<:ProjectedEntangledPair{P,Periodic}}, order, _, _) where {P<:Plug} = tuple(order...)
-
-defaultorder(::Type{<:ProjectedEntangledPair{State}}) = (:l, :r, :u, :d, :o)
-defaultorder(::Type{<:ProjectedEntangledPair{Operator}}) = (:l, :r, :u, :d, :i, :o)
-
-"""
-    ProjectedEntangledPair{P,B}(arrays::Matrix{AbstractArray}; χ::Union{Nothing,Int} = nothing, order = defaultorder(ProjectedEntangledPair{P}))
-
-Construct a [`TensorNetwork`](@ref) with [`ProjectedEntangledPair`](@ref) ansatz, from the arrays of the tensors.
-
-# Keyword Arguments
-
-  - `order` Order of the tensor indices on `arrays`. Defaults to `(:l, :r, :u, :d, :o)` if `P` is a `State`, `(:l, :r, :u, :d, :i, :o)` if `Operator`.
-"""
-function ProjectedEntangledPair{P,B}(
-    arrays;
-    χ = nothing,
-    order = defaultorder(ProjectedEntangledPair{P}),
-    metadata...,
-) where {P<:Plug,B<:Boundary}
-    issetequal(order, defaultorder(ProjectedEntangledPair{P})) || throw(
-        ArgumentError(
-            "`order` must be a permutation of $(join(String.(defaultorder(ProjectedEntangledPair{P})), ',', " and "))",
-        ),
-    )
-
-    m, n = size(arrays)
-    hinds = Dict((i, j) => Symbol(uuid4()) for i in 1:m, j in ringpeek(1:n))
-    vinds = Dict((i, j) => Symbol(uuid4()) for i in ringpeek(1:m), j in 1:n)
-    oinds = Dict((i, j) => Symbol(uuid4()) for i in 1:m, j in 1:n)
-    iinds = Dict((i, j) => Symbol(uuid4()) for i in 1:m, j in 1:n)
-
-    input, output = if P <: Property
-        Symbol[], Symbol[]
-    elseif P <: State
-        Symbol[], vec([oinds[i, j] for i in 1:m, j in 1:n])
-    elseif P <: Operator
-        vec([iinds[i, j] for i in 1:m, j in 1:n]), vec([oinds[i, j] for i in 1:m, j in 1:n])
-    else
-        throw(ArgumentError("Plug $P is not valid"))
-    end
-
-    tensors::Vector{Tensor} =
-        map(zip(Iterators.map(Tuple, eachindex(IndexCartesian(), arrays)), arrays)) do ((i, j), array)
-            dirs = sitealias(ProjectedEntangledPair{P,B}, order, (m, n), (i, j))
-
-            inds = map(dirs) do dir
-                if dir === :l
-                    hinds[(i, (mod1(j - 1, n), j))]
-                elseif dir === :r
-                    hinds[(i, (j, mod1(j + 1, n)))]
-                elseif dir === :u
-                    vinds[((mod1(i - 1, m), i), j)]
-                elseif dir === :d
-                    vinds[((i, mod1(i + 1, m)), j)]
-                elseif dir === :i
-                    iinds[(i, j)]
-                elseif dir === :o
-                    oinds[(i, j)]
-                end
-            end
-
-            Tensor(array, inds)
-        end |> vec
-
-    return QuantumTensorNetwork(TensorNetwork(tensors), input, output)
-end
-
-# TODO normalize
-# TODO let choose the orthogonality center
-# TODO different input/output physical dims
-function Base.rand(rng::Random.AbstractRNG, sampler::QTNSampler{ProjectedEntangledPair{P,Open}}) where {P<:Plug}
-    rows = sampler.rows
-    cols = sampler.cols
-    χ = sampler.χ
-    p = get(sampler, :p, 2)
-    T = get(sampler, :eltype, Float64)
-
-    arrays::Matrix{AbstractArray{T,N} where {N}} = reshape(
-        map(Iterators.product(1:rows, 1:cols)) do (i, j)
-            shape = filter(
-                !=(1),
-                [
-                    i === 1 ? 1 : χ,
-                    i === rows ? 1 : χ,
-                    j === 1 ? 1 : χ,
-                    j === cols ? 1 : χ,
-                    p,
-                    if P <: State
-                        1
-                    elseif P <: Operator
-                        p
-                    else
-                        throw(ErrorException("$P is not a valid Plug type"))
-                    end,
-                ],
-            )
-
-            rand(rng, T, shape...)
-        end,
-        rows,
-        cols,
-    )
-
-    # normalize state
-    arrays[1, 1] ./= P <: State ? sqrt(p) : p
-
-    ProjectedEntangledPair{P,Open}(arrays)
-end
-
-# TODO normalize
-# TODO let choose the orthogonality center
-# TODO different input/output physical dims
-function Base.rand(rng::Random.AbstractRNG, sampler::QTNSampler{ProjectedEntangledPair{P,Periodic}}) where {P<:Plug}
-    rows = sampler.rows
-    cols = sampler.cols
-    χ = sampler.χ
-    p = get(sampler, :p, 2)
-    T = get(sampler, :eltype, Float64)
-
-    arrays::Matrix{AbstractArray{T,N} where {N}} = reshape(
-        map(Iterators.product(1:rows, 1:cols)) do (i, j)
-            shape = tuple([χ, χ, χ, χ]..., ([if P <: State
-                (p,)
-            elseif P <: Operator
-                (p, p)
-            else
-                throw(ErrorException("$P is not a valid Plug type"))
-            end]...)...)
-
-            # A = gramschmidt!(rand(rng, T, shape[1], prod(shape[1:end])))
-            A = rand(rng, T, shape...)
-        end,
-        rows,
-        cols,
-    )
-
-    # normalize state
-    arrays[1, 1] ./= P <: State ? sqrt(p) : p
-
-    ProjectedEntangledPair{P,Periodic}(arrays)
-end
diff --git a/src/Quantum/Quantum.jl b/src/Quantum/Quantum.jl
deleted file mode 100644
index 180aa854..00000000
--- a/src/Quantum/Quantum.jl
+++ /dev/null
@@ -1,258 +0,0 @@
-using LinearAlgebra
-using UUIDs: uuid4
-using ValSplit
-using Classes
-
-"""
-    QuantumTensorNetwork
-
-Tensor Network that has a notion of sites and directionality (input/output).
-"""
-@class QuantumTensorNetwork <: TensorNetwork begin
-    input::Vector{Symbol}
-    output::Vector{Symbol}
-end
-
-inds(tn::absclass(QuantumTensorNetwork), ::Val{:in}) = tuple(tn.input...)
-inds(tn::absclass(QuantumTensorNetwork), ::Val{:in}, site) = tn.input[site]
-inds(tn::absclass(QuantumTensorNetwork), ::Val{:out}) = tuple(tn.output...)
-inds(tn::absclass(QuantumTensorNetwork), ::Val{:out}, site) = tn.output[site]
-inds(tn::absclass(QuantumTensorNetwork), ::Val{:physical}) = ∪(tn.input, tn.output)
-inds(tn::absclass(QuantumTensorNetwork), ::Val{:virtual}) = setdiff(inds(tn, Val(:all)), inds(tn, Val(:physical)))
-
-"""
-    sites(tn::AbstractQuantumTensorNetwork, dir)
-
-Return the sites in which the [`TensorNetwork`](@ref) acts.
-"""
-sites(tn::absclass(QuantumTensorNetwork)) = sites(tn, :in) ∪ sites(tn, :out)
-function sites(tn::absclass(QuantumTensorNetwork), dir)
-    if dir === :in
-        firstindex(tn.input):lastindex(tn.input)
-    elseif dir === :out
-        firstindex(tn.output):lastindex(tn.output)
-    else
-        throw(MethodError("unknown dir=$dir"))
-    end
-end
-
-function Base.replace!(tn::absclass(QuantumTensorNetwork), old_new::Pair{Symbol,Symbol})
-    Base.@invoke replace!(tn::absclass(TensorNetwork), old_new::Pair{Symbol,Symbol})
-
-    replace!(tn.input, old_new)
-    replace!(tn.output, old_new)
-
-    return tn
-end
-
-"""
-    adjoint(tn::AbstractQuantumTensorNetwork)
-
-Return the adjoint [`TensorNetwork`](@ref).
-
-# Implementation details
-
-The tensors are not transposed, just `conj!` is applied to them.
-"""
-function Base.adjoint(tn::absclass(QuantumTensorNetwork))
-    tn = deepcopy(tn)
-
-    # swap input/output
-    temp = copy(tn.input)
-    resize!(tn.input, length(tn.output))
-    copy!(tn.input, tn.output)
-    resize!(tn.output, length(temp))
-    copy!(tn.output, temp)
-
-    foreach(conj!, tensors(tn))
-
-    return tn
-end
-
-function Base.merge!(self::absclass(QuantumTensorNetwork), other::absclass(QuantumTensorNetwork))
-    sites(self, :out) == sites(other, :in) ||
-        throw(DimensionMismatch("both `QuantumTensorNetwork`s must contain the same set of sites"))
-
-    # copy to avoid mutation if reindex is needed
-    # TODO deepcopy because `indices` are not correctly copied and it mutates
-    other = deepcopy(other)
-
-    # reindex other if needed
-    if inds(self, set = :out) != inds(other, set = :in)
-        replace!(other, map(splat(=>), zip(inds(other, set = :in), inds(self, set = :out))))
-    end
-
-    # reindex inner indices of `other` to avoid accidental hyperindices
-    conflict = inds(self, set = :virtual) ∩ inds(other, set = :virtual)
-    if !isempty(conflict)
-        replace!(other, map(i -> i => Symbol(uuid4()), conflict))
-    end
-
-    @invoke merge!(self::absclass(TensorNetwork), other::absclass(TensorNetwork))
-
-    # update i/o
-    copy!(self.output, other.output)
-
-    self
-end
-
-function contract(a::absclass(QuantumTensorNetwork), b::absclass(QuantumTensorNetwork); kwargs...)
-    contract(merge(a, b); kwargs...)
-end
-
-# Plug trait
-abstract type Plug end
-struct Property <: Plug end
-struct State <: Plug end
-struct Dual <: Plug end
-struct Operator <: Plug end
-
-"""
-    plug(::QuantumTensorNetwork)
-
-Return the `Plug` type of the [`TensorNetwork`](@ref). The following `Plug`s are defined in `Tenet`:
-
-  - `Property` No inputs nor outputs.
-  - `State` Only outputs.
-  - `Dual` Only inputs.
-  - `Operator` Inputs and outputs.
-"""
-function plug(tn)
-    if isempty(tn.input) && isempty(tn.output)
-        Property()
-    elseif isempty(tn.input)
-        State()
-    elseif isempty(tn.output)
-        Dual()
-    else
-        Operator()
-    end
-end
-
-# TODO look for more stable ways
-"""
-    norm(ψ::AbstractQuantumTensorNetwork, p::Real=2)
-
-Compute the ``p``-norm of a [`QuantumTensorNetwork`](@ref).
-
-See also: [`normalize!`](@ref).
-"""
-function LinearAlgebra.norm(ψ::absclass(QuantumTensorNetwork), p::Real = 2; kwargs...)
-    p == 2 || throw(ArgumentError("p=$p is not implemented yet"))
-
-    tn = merge(ψ, ψ')
-    if plug(tn) isa Operator
-        tn = tr(tn)
-    end
-
-    return contract(tn; kwargs...) |> only |> sqrt |> abs
-end
-
-"""
-    normalize!(ψ::AbstractQuantumTensorNetwork, p::Real = 2; insert::Union{Nothing,Int} = nothing)
-
-In-place normalize the [`TensorNetwork`](@ref).
-
-# Keyword Arguments
-
-  - `insert` Choose the way the normalization is performed:
-
-      + If `insert=nothing` (default), then all tensors are divided by ``\\sqrt[n]{\\lVert \\psi \\rVert_p}`` where `n` is the number of tensors.
-      + If `insert isa Integer`, then the tensor connected to the site pointed by `insert` is divided by the norm.
-
-    Both approaches are mathematically equivalent. Choose between them depending on the numerical properties.
-
-See also: [`norm`](@ref).
-"""
-function LinearAlgebra.normalize!(
-    ψ::absclass(QuantumTensorNetwork),
-    p::Real = 2;
-    insert::Union{Nothing,Int} = nothing,
-    kwargs...,
-)
-    norm = LinearAlgebra.norm(ψ, p; kwargs...)
-
-    if isnothing(insert)
-        # method 1: divide all tensors by (√v)^(1/n)
-        n = length(tensors(ψ))
-        norm ^= 1 / n
-        for tensor in tensors(ψ)
-            tensor ./= norm
-        end
-    else
-        # method 2: divide only one tensor
-        tensor = ψ.tensors[insert] # tensors(ψ, insert) # TODO fix this to match site?
-        tensor ./= norm
-    end
-end
-
-"""
-    LinearAlgebra.tr(U::AbstractQuantumTensorNetwork)
-
-Trace `U`: sum of diagonal elements if `U` is viewed as a matrix.
-
-Depending on the result of `plug(U)`, different actions can be taken:
-
-  - If `Property()`, the result of `contract(U)` will be a "scalar", for which the trace acts like the identity.
-  - If `State()`, the result of `contract(U)` will be a "vector", for which the trace is undefined and will fail.
-  - If `Operator()`, the input and output indices of `U` are connected.
-"""
-LinearAlgebra.tr(U::absclass(QuantumTensorNetwork)) = tr!(U)
-tr!(U::absclass(QuantumTensorNetwork)) = tr!(plug(U), U)
-tr!(::Property, scalar::absclass(QuantumTensorNetwork)) = scalar
-function tr!(::Operator, U::absclass(QuantumTensorNetwork))
-    sites(U, :in) == sites(U, :out) || throw(ArgumentError("input and output sites do not match"))
-    copyto!(U.output, U.input)
-    U
-end
-
-"""
-    fidelity(ψ,ϕ)
-
-Compute the fidelity between states ``\\ket{\\psi}`` and ``\\ket{\\phi}``.
-"""
-fidelity(a, b; kwargs...) = abs(only(contract(a, b'; kwargs...)))^2
-
-"""
-    marginal(ψ, site)
-
-Return the marginal quantum state of site.
-"""
-function marginal(ψ, site)
-    plug(ψ) == State() || throw("unimplemented")
-
-    siteindex = inds(ψ, :out, site)
-    tensor = only(select(tn, siteindex))
-    sum(tensor, inds = setdiff(inds(tensor), [siteindex]))
-end
-
-# Boundary trait
-abstract type Boundary end
-struct Open <: Boundary end
-struct Periodic <: Boundary end
-
-"""
-    boundary(::QuantumTensorNetwork)
-
-Return the `Boundary` type of the [`TensorNetwork`](@ref). The following `Boundary`s are defined in `Tenet`:
-
-  - `Open`
-  - `Periodic`
-"""
-function boundary end
-
-abstract type Ansatz end
-
-struct QTNSampler{A<:Ansatz} <: Random.Sampler{QuantumTensorNetwork}
-    config::Dict{Symbol,Any}
-
-    QTNSampler{A}(; kwargs...) where {A} = new{A}(kwargs)
-end
-
-Base.eltype(::QTNSampler{A}) where {A} = A
-
-Base.getproperty(obj::QTNSampler, name::Symbol) = name === :config ? getfield(obj, :config) : obj.config[name]
-Base.get(obj::QTNSampler, name, default) = get(obj.config, name, default)
-
-Base.rand(A::Type{<:Ansatz}; kwargs...) = rand(Random.default_rng(), A; kwargs...)
-Base.rand(rng::AbstractRNG, A::Type{<:Ansatz}; kwargs...) = rand(rng, QTNSampler{A}(; kwargs...))
diff --git a/src/Tenet.jl b/src/Tenet.jl
index bbe52d09..48943968 100644
--- a/src/Tenet.jl
+++ b/src/Tenet.jl
@@ -16,20 +16,6 @@ export contract, contract!
 include("Transformations.jl")
 export transform, transform!
 
-include("Quantum/Quantum.jl")
-export QuantumTensorNetwork, sites, fidelity
-export Plug, plug, Property, State, Dual, Operator
-export Boundary, boundary, Open, Periodic
-
-include("Quantum/MP.jl")
-export MatrixProduct, MPS, MPO
-
-include("Quantum/PEP.jl")
-export ProjectedEntangledPair, PEPS, PEPO
-
-# reexports from LinearAlgebra
-export norm, normalize!
-
 # reexports from EinExprs
 export einexpr, inds
 
diff --git a/test/MatrixProductOperator_test.jl b/test/MatrixProductOperator_test.jl
deleted file mode 100644
index 97cc6de2..00000000
--- a/test/MatrixProductOperator_test.jl
+++ /dev/null
@@ -1,126 +0,0 @@
-@testset "MatrixProduct{Operator}" begin
-    @testset "plug" begin
-        @test plug(MatrixProduct{Operator}) === Operator()
-        @test all(T -> plug(MatrixProduct{Operator,T}) === Operator(), [Open, Periodic])
-    end
-
-    @testset "boundary" begin
-        @test all(B -> boundary(MatrixProduct{Operator,B}) == B(), [Open, Periodic])
-    end
-
-    @testset "Constructor" begin
-        # empty constructor
-        @test_throws Exception MatrixProduct{Operator}([])
-
-        @test begin
-            arrays = [rand(2, 2, 2)]
-            MatrixProduct{Operator}(arrays) isa QuantumTensorNetwork
-        end
-
-        @test begin
-            arrays = [rand(2, 2, 2), rand(2, 2, 2)]
-            MatrixProduct{Operator}(arrays) isa QuantumTensorNetwork
-        end
-
-        @testset "`Open` boundary" begin
-            # product operator
-            @test begin
-                arrays = [rand(1, 2, 2), rand(1, 1, 2, 2), rand(1, 2, 2)]
-                MatrixProduct{Operator,Open}(arrays) isa QuantumTensorNetwork
-            end
-
-            # alternative constructor
-            @test begin
-                arrays = [rand(1, 2, 2), rand(1, 1, 2, 2), rand(1, 2, 2)]
-                MatrixProduct{Operator}(arrays; boundary = Open) isa QuantumTensorNetwork
-            end
-
-            # entangling operator
-            @test begin
-                i = 3
-                o = 5
-                arrays = [rand(2, i, o), rand(2, 4, i, o), rand(4, i, o)]
-                MatrixProduct{Operator,Open}(arrays) isa QuantumTensorNetwork
-            end
-
-            # entangling operator - change order
-            @test begin
-                i = 3
-                o = 5
-                arrays = [rand(i, 2, o), rand(2, i, 4, o), rand(4, i, o)]
-                MatrixProduct{Operator,Open}(arrays, order = (:l, :i, :r, :o)) isa QuantumTensorNetwork
-            end
-
-            # fail on Open with Periodic format
-            @test_throws MethodError begin
-                arrays = [rand(1, 1, 2, 2), rand(1, 1, 2, 2), rand(1, 1, 2, 2)]
-                MatrixProduct{Operator,Open}(arrays) isa QuantumTensorNetwork
-            end
-        end
-
-        @testset "`Periodic` boundary" begin
-            # product operator
-            @test begin
-                arrays = [rand(1, 1, 2, 2), rand(1, 1, 2, 2), rand(1, 1, 2, 2)]
-                MatrixProduct{Operator,Periodic}(arrays) isa QuantumTensorNetwork
-            end
-
-            # alternative constructor
-            @test begin
-                arrays = [rand(1, 1, 2, 2), rand(1, 1, 2, 2), rand(1, 1, 2, 2)]
-                MatrixProduct{Operator}(arrays; boundary = Periodic) isa QuantumTensorNetwork
-            end
-
-            # entangling operator
-            @test begin
-                i = 3
-                o = 5
-                arrays = [rand(2, 4, i, o), rand(4, 8, i, o), rand(8, 2, i, o)]
-                MatrixProduct{Operator,Periodic}(arrays) isa QuantumTensorNetwork
-            end
-
-            # entangling operator - change order
-            @test begin
-                i = 3
-                o = 5
-                arrays = [rand(2, i, 4, o), rand(4, i, 8, o), rand(8, i, 2, o)]
-                MatrixProduct{Operator,Periodic}(arrays, order = (:l, :i, :r, :o)) isa QuantumTensorNetwork
-            end
-
-            # fail on Periodic with Open format
-            @test_throws MethodError begin
-                arrays = [rand(1, 2, 2), rand(1, 1, 2, 2), rand(1, 2, 2)]
-                MatrixProduct{Operator,Periodic}(arrays) isa QuantumTensorNetwork
-            end
-        end
-    end
-
-    @testset "merge" begin
-        @test begin
-            arrays = [rand(2, 2), rand(2, 2)]
-            mps = MatrixProduct{State,Open}(arrays)
-            arrays_o = [rand(2, 2, 2), rand(2, 2, 2)]
-            mpo = MatrixProduct{Operator}(arrays_o)
-            merge(mps, mpo) isa QuantumTensorNetwork
-        end
-
-        @test begin
-            arrays = [rand(2, 2), rand(2, 2)]
-            mps = MatrixProduct{State,Open}(arrays)
-            arrays_o = [rand(2, 2, 2), rand(2, 2, 2)]
-            mpo = MatrixProduct{Operator}(arrays_o)
-            merge(mpo, mps') isa QuantumTensorNetwork
-        end
-
-        @test begin
-            arrays = [rand(2, 2, 2), rand(2, 2, 2)]
-            mpo = MatrixProduct{Operator}(arrays)
-            merge(mpo, mpo') isa QuantumTensorNetwork
-        end
-    end
-
-    @testset "norm" begin
-        mpo = rand(MatrixProduct{Operator,Open}, n = 8, p = 2, χ = 8)
-        @test norm(mpo) ≈ 1
-    end
-end
diff --git a/test/MatrixProductState_test.jl b/test/MatrixProductState_test.jl
deleted file mode 100644
index c3459a66..00000000
--- a/test/MatrixProductState_test.jl
+++ /dev/null
@@ -1,122 +0,0 @@
-@testset "MatrixProduct{State}" begin
-    @testset "plug" begin
-        @test plug(MatrixProduct{State}) == State()
-        @test all(T -> plug(MatrixProduct{State,T}) == State(), [Open, Periodic])
-    end
-
-    @testset "boundary" begin
-        @test all(B -> boundary(MatrixProduct{State,B}) == B(), [Open, Periodic])
-    end
-
-    @testset "Constructor" begin
-        # empty constructor
-        @test_throws Exception MatrixProduct{State}([])
-
-        @test begin
-            arrays = [rand(1, 2)]
-            MatrixProduct{State}(arrays) isa QuantumTensorNetwork
-        end
-
-        @test begin
-            arrays = [rand(1, 2), rand(1, 2)]
-            MatrixProduct{State}(arrays) isa QuantumTensorNetwork
-        end
-
-        @testset "`Open` boundary" begin
-            # product state
-            @test begin
-                arrays = [rand(1, 2), rand(1, 1, 2), rand(1, 2)]
-                MatrixProduct{State,Open}(arrays) isa QuantumTensorNetwork
-            end
-
-            # entangled state
-            @test begin
-                arrays = [rand(2, 2), rand(2, 4, 2), rand(4, 1, 2), rand(1, 2)]
-                MatrixProduct{State,Open}(arrays) isa QuantumTensorNetwork
-            end
-
-            @testset "custom order" begin
-                arrays = [rand(3, 1), rand(3, 1, 3), rand(1, 3)]
-                ψ = MatrixProduct{State,Open}(arrays, order = (:r, :o, :l))
-
-                @test ψ isa QuantumTensorNetwork
-            end
-
-            # alternative constructor
-            @test begin
-                arrays = [rand(1, 2), rand(1, 1, 2), rand(1, 2)]
-                MatrixProduct{State}(arrays; boundary = Open) isa QuantumTensorNetwork
-            end
-
-            # fail on Open with Periodic format
-            @test_throws Exception begin
-                arrays = [rand(1, 1, 2), rand(1, 1, 2), rand(1, 1, 2)]
-                MatrixProduct{State,Open}(arrays) isa QuantumTensorNetwork
-            end
-
-            @testset "rand" begin
-                # 4 => χ < maximum possible χ for the given parameters
-                # 32 => χ > maximum possible χ for the given parameters
-                @testset "χ = $χ" for χ in [4, 32]
-                    ψ = rand(MatrixProduct{State,Open}, n = 7, p = 2, χ = χ)
-
-                    @test ψ isa QuantumTensorNetwork
-                    @test length(tensors(ψ)) == 7
-                    @test maximum(vind -> size(ψ, vind), inds(ψ, :inner)) <= 32
-                end
-            end
-        end
-
-        @testset "`Periodic` boundary" begin
-            # product state
-            @test begin
-                arrays = [rand(1, 1, 2), rand(1, 1, 2), rand(1, 1, 2)]
-                MatrixProduct{State,Periodic}(arrays) isa QuantumTensorNetwork
-            end
-
-            # entangled state
-            @test begin
-                arrays = [rand(3, 4, 2), rand(4, 8, 2), rand(8, 3, 2)]
-                MatrixProduct{State,Periodic}(arrays) isa QuantumTensorNetwork
-            end
-
-            @testset "custom order" begin
-                arrays = [rand(3, 1, 3), rand(3, 1, 3), rand(3, 1, 3)]
-                ψ = MatrixProduct{State,Periodic}(arrays, order = (:r, :o, :l))
-
-                @test ψ isa QuantumTensorNetwork
-            end
-
-            # alternative constructor
-            @test begin
-                arrays = [rand(1, 1, 2), rand(1, 1, 2), rand(1, 1, 2)]
-                MatrixProduct{State}(arrays; boundary = Periodic) isa QuantumTensorNetwork
-            end
-
-            # fail on Periodic with Open format
-            @test_throws Exception begin
-                arrays = [rand(1, 2), rand(1, 1, 2), rand(1, 2)]
-                MatrixProduct{State,Periodic}(arrays) isa QuantumTensorNetwork
-            end
-        end
-    end
-
-    @testset "merge" begin
-        @test begin
-            arrays = [rand(2, 2), rand(2, 2)]
-            mps = MatrixProduct{State,Open}(arrays)
-            merge(mps, mps') isa QuantumTensorNetwork
-        end
-
-        @test begin
-            arrays = [rand(1, 1, 2), rand(1, 1, 2)]
-            mps = MatrixProduct{State,Periodic}(arrays)
-            merge(mps, mps') isa QuantumTensorNetwork
-        end
-    end
-
-    @testset "norm" begin
-        mps = rand(MatrixProduct{State,Open}, n = 8, p = 2, χ = 8)
-        @test norm(mps) ≈ 1
-    end
-end
diff --git a/test/Project.toml b/test/Project.toml
index f186cef7..4221fac3 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -10,7 +10,6 @@ Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"
 OMEinsum = "ebe7aa44-baf0-506c-a96f-8464559b3922"
 Permutations = "2ae35dd2-176d-5d53-8349-f30d82d94d4f"
-Quac = "b9105292-1415-45cf-bff1-d6ccf71e6143"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
diff --git a/test/Quantum_test.jl b/test/Quantum_test.jl
deleted file mode 100644
index c7aa2f28..00000000
--- a/test/Quantum_test.jl
+++ /dev/null
@@ -1,152 +0,0 @@
-@testset "Quantum" begin
-    state = QuantumTensorNetwork(
-        TensorNetwork([Tensor(rand(2, 2), (:i, :k)), Tensor(rand(3, 2, 4), (:j, :k, :l))]),
-        Symbol[], # input
-        [:i, :j], # output
-    )
-
-    operator = QuantumTensorNetwork(
-        TensorNetwork([Tensor(rand(2, 4, 2), (:a, :c, :d)), Tensor(rand(3, 4, 3, 5), (:b, :c, :e, :f))]),
-        [:a, :b], # input
-        [:d, :e], # output
-    )
-
-    @testset "adjoint" begin
-        @testset "State" begin
-            adj = adjoint(state)
-            @test adj.input == state.output
-            @test adj.output == state.input
-            @test all(((a, b),) -> a == conj(b), zip(tensors(state), tensors(adj)))
-        end
-
-        @testset "Operator" begin
-            adj = adjoint(operator)
-            @test adj.input == operator.output
-            @test adj.output == operator.input
-            @test all(((a, b),) -> a == conj(b), zip(tensors(operator), tensors(adj)))
-        end
-    end
-
-    @testset "plug" begin
-        @test plug(state) == State()
-        @test plug(state') == Dual()
-        @test plug(operator) == Operator()
-    end
-
-    @testset "sites" begin
-        @test issetequal(sites(state), [1, 2])
-        @test issetequal(sites(operator), [1, 2])
-    end
-
-    @testset "inds" begin
-        @testset "State" begin
-            @test issetequal(inds(state), [:i, :j, :k, :l])
-            @test issetequal(inds(state, set = :open), [:i, :j, :l])
-            @test issetequal(inds(state, set = :inner), [:k])
-            @test isempty(inds(state, set = :hyper))
-            @test isempty(inds(state, set = :in))
-            @test issetequal(inds(state, set = :out), [:i, :j])
-            @test issetequal(inds(state, set = :physical), [:i, :j])
-            @test issetequal(inds(state, set = :virtual), [:k, :l])
-        end
-
-        @testset "Operator" begin
-            @test issetequal(inds(operator), [:a, :b, :c, :d, :e, :f])
-            @test issetequal(inds(operator, set = :open), [:a, :b, :d, :e, :f])
-            @test issetequal(inds(operator, set = :inner), [:c])
-            @test isempty(inds(operator, set = :hyper))
-            @test issetequal(inds(operator, set = :in), [:a, :b])
-            @test issetequal(inds(operator, set = :out), [:d, :e])
-            @test issetequal(inds(operator, set = :physical), [:a, :b, :d, :e])
-            @test issetequal(inds(operator, set = :virtual), [:c, :f])
-        end
-    end
-
-    @testset "merge" begin
-        @testset "(State, State)" begin
-            tn = merge(state, state')
-
-            @test plug(tn) == Property()
-
-            @test isempty(sites(tn, :in))
-            @test isempty(sites(tn, :out))
-
-            @test isempty(inds(tn, set = :in))
-            @test isempty(inds(tn, set = :out))
-            @test isempty(inds(tn, set = :physical))
-            @test issetequal(inds(tn), inds(tn, set = :virtual))
-        end
-
-        @testset "(State, Operator)" begin
-            tn = merge(state, operator)
-
-            @test plug(tn) == State()
-
-            @test isempty(sites(tn, :in))
-            @test issetequal(sites(tn, :out), sites(operator, :out))
-
-            @test isempty(inds(tn, set = :in))
-            @test issetequal(inds(tn, set = :out), inds(operator, :out))
-            @test issetequal(inds(tn, set = :physical), inds(operator, :out))
-            @test issetequal(inds(tn, set = :virtual), inds(state) ∪ inds(operator, :virtual))
-        end
-
-        @testset "(Operator, State)" begin
-            tn = merge(operator, state')
-
-            @test plug(tn) == Dual()
-
-            @test issetequal(sites(tn, :in), sites(operator, :in))
-            @test isempty(sites(tn, :out))
-
-            @test issetequal(inds(tn, set = :in), inds(operator, :in))
-            @test isempty(inds(tn, set = :out))
-            @test issetequal(inds(tn, set = :physical), inds(operator, :in))
-            @test issetequal(
-                inds(tn, set = :virtual),
-                inds(state, :virtual) ∪ inds(operator, :virtual) ∪ inds(operator, :out),
-            )
-        end
-
-        @testset "(Operator, Operator)" begin
-            tn = merge(operator, operator')
-
-            @test plug(tn) == Operator()
-
-            @test issetequal(sites(tn, :in), sites(operator, :in))
-            @test issetequal(sites(tn, :out), sites(operator, :in))
-
-            @test issetequal(inds(tn, set = :in), inds(operator, :in))
-            @test issetequal(inds(tn, set = :out), inds(operator, :in))
-            @test issetequal(inds(tn, set = :physical), inds(operator, :in))
-            @test inds(operator, :virtual) ⊆ inds(tn, set = :virtual)
-        end
-
-        @testset "(Operator, Operator)" begin
-            tn = merge(operator', operator)
-
-            @test plug(tn) == Operator()
-
-            @test issetequal(sites(tn, :in), sites(operator, :out))
-            @test issetequal(sites(tn, :out), sites(operator, :out))
-
-            @test issetequal(inds(tn, set = :in), inds(operator, :out))
-            @test issetequal(inds(tn, set = :out), inds(operator, :out))
-            @test issetequal(inds(tn, set = :physical), inds(operator, :out))
-            @test inds(operator, :virtual) ⊆ inds(tn, set = :virtual)
-        end
-
-        @testset "(State, Operator, State)" begin
-            tn = merge(state, operator, state')
-
-            @test plug(tn) == Property()
-
-            @test isempty(sites(tn, :in))
-            @test isempty(sites(tn, :out))
-
-            @test isempty(inds(tn, set = :in))
-            @test isempty(inds(tn, set = :out))
-            @test isempty(inds(tn, set = :physical))
-        end
-    end
-end
diff --git a/test/integration/Quac_test.jl b/test/integration/Quac_test.jl
deleted file mode 100644
index 1b5179ba..00000000
--- a/test/integration/Quac_test.jl
+++ /dev/null
@@ -1,25 +0,0 @@
-@testset "Quac" begin
-    using Quac
-    using UUIDs: uuid4
-
-    @testset "Constructor" begin
-        n = 2
-        qft = Quac.Algorithms.QFT(n)
-        tn = QuantumTensorNetwork(qft)
-
-        @test tn isa QuantumTensorNetwork
-        @test issetequal(sites(tn), 1:n)
-    end
-
-    # TODO currently broken
-    @testset "merge" begin
-        n = 2
-        qft = QuantumTensorNetwork(Quac.Algorithms.QFT(n))
-        iqft = replace(qft, [index => Symbol(uuid4()) for index in inds(qft)]...)
-
-        tn = merge(qft, iqft)
-
-        @test tn isa QuantumTensorNetwork
-        @test issetequal(sites(tn), 1:2)
-    end
-end
diff --git a/test/runtests.jl b/test/runtests.jl
index f35ac9ce..e63c5589 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -2,7 +2,7 @@ using Test
 using Tenet
 using OMEinsum
 
-@testset "Core tests" verbose = true begin
+@testset "Unit tests" verbose = true begin
     include("Helpers_test.jl")
     include("Tensor_test.jl")
     include("Numerics_test.jl")
@@ -10,16 +10,9 @@ using OMEinsum
     include("Transformations_test.jl")
 end
 
-@testset "Quantum tests" verbose = true begin
-    include("Quantum_test.jl")
-    include("MatrixProductState_test.jl")
-    include("MatrixProductOperator_test.jl")
-end
-
 @testset "Integration tests" verbose = true begin
     include("integration/ChainRules_test.jl")
     include("integration/BlockArray_test.jl")
-    include("integration/Quac_test.jl")
     include("integration/Makie_test.jl")
 end