Stabilize tests by fixing seeds, reduce verbosity, use more elegant H…

…eisenberg Hamiltonian

Project.toml

@@ -10,10 +10,10 @@ Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MPSKit = "bb1c41ca-d63c-52ed-829e-0820dda26502"
-MPSKitModels = "ca635005-6f8c-4cd1-b51d-8491250ef2ab"
 OptimKit = "77e91f04-9b3b-57a6-a776-40b61faaebe0"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 TensorKit = "07d1fe3e-3e46-537d-9eac-e9e13d0d4cec"
 VectorInterface = "409d34a3-91d5-4945-b6ec-7529ddf182d8"
@@ -34,8 +34,8 @@ Zygote = "0.6"
 julia = "1.6"
 
 [extras]
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
 test = ["Test", "SafeTestsets"]

examples/heisenberg.jl

@@ -1,24 +1,19 @@
 using LinearAlgebra
-using TensorKit, MPSKitModels, OptimKit
+using TensorKit, OptimKit
 using PEPSKit, KrylovKit
 
 # Square lattice Heisenberg Hamiltonian
 # We use the parameters (J₁, J₂, J₃) = (-1, 1, -1) by default to capture
 # the ground state in a single-site unit cell. This can be seen from
 # sublattice rotating H from parameters (1, 1, 1) to (-1, 1, -1).
 function square_lattice_heisenberg(; Jx=-1, Jy=1, Jz=-1)
-    Sx, Sy, Sz, _ = spinmatrices(1//2)
-    Vphys = ℂ^2
-    σx = TensorMap(Sx, Vphys, Vphys)
-    σy = TensorMap(Sy, Vphys, Vphys)
-    σz = TensorMap(Sz, Vphys, Vphys)
-
-    @tensor H[-1 -3; -2 -4] :=
-        Jx * σx[-1, -2] * σx[-3, -4] +
-        Jy * σy[-1, -2] * σy[-3, -4] +
-        Jz * σz[-1, -2] * σz[-3, -4]
-
-    return NLocalOperator{NearestNeighbor}(H)
+    physical_space = ComplexSpace(2)
+    T = ComplexF64
+    σx = TensorMap(T[0 1; 1 0], physical_space, physical_space)
+    σy = TensorMap(T[0 im; -im 0], physical_space, physical_space)
+    σz = TensorMap(T[1 0; 0 -1], physical_space, physical_space)
+    H = (Jx * σx ⊗ σx) + (Jy * σy ⊗ σy) + (Jz * σz ⊗ σz)
+    return NLocalOperator{NearestNeighbor}(H / 4)
 end
 
 # Parameters

test/boundarymps/vumps.jl

@@ -1,19 +1,21 @@
 using Test
+using Random
 using PEPSKit
 using TensorKit
 using MPSKit
 using LinearAlgebra
 
+Random.seed!(29384293742893)
 psi = InfinitePEPS(ComplexSpace(2), ComplexSpace(2))
 
 T = PEPSKit.InfiniteTransferPEPS(psi, 1, 1)
 mps = PEPSKit.initializeMPS(T, [ComplexSpace(8)])
 
-mps, envs, ϵ = leading_boundary(mps, T, VUMPS())
+mps, envs, ϵ = leading_boundary(mps, T, VUMPS(; verbosity=2))
 N = sum(expectation_value(mps, T))
 
 ctm = leading_boundary(
-    psi, CTMRG(; verbosity=2, fixedspace=true), CTMRGEnv(psi; Venv=ComplexSpace(8))
+    psi, CTMRG(; verbosity=1, fixedspace=true), CTMRGEnv(psi; Venv=ComplexSpace(8))
 )
 N2 = norm(psi, ctm)
 

test/ctmrg/gaugefix.jl

@@ -1,4 +1,5 @@
 using Test
+using Random
 using PEPSKit
 using TensorKit
 
@@ -17,6 +18,7 @@ end
 
 @testset "Trivial symmetry ($T) - ($unitcell)" for (T, unitcell) in
                                                    Iterators.product(scalartypes, unitcells)
+    Random.seed!(12345678)
     physical_space = ComplexSpace(2)
     peps_space = ComplexSpace(2)
     ctm_space = ComplexSpace(16)
@@ -25,17 +27,11 @@ end
     psi = _make_symmetric(psi)
     ctm = CTMRGEnv(psi; Venv=ctm_space)
 
+    verbosity = 1
     alg = CTMRG(;
-        trscheme=truncdim(dim(ctm_space)), tol=1e-10, miniter=4, maxiter=100, verbosity=2
-    )
-    alg_fixed = CTMRG(;
-        trscheme=truncdim(dim(ctm_space)),
-        tol=1e-10,
-        miniter=4,
-        maxiter=100,
-        verbosity=2,
-        fixedspace=true,
+        trscheme=truncdim(dim(ctm_space)), tol=1e-10, miniter=4, maxiter=200, verbosity
     )
+    alg_fixed = CTMRG(; trscheme=truncdim(dim(ctm_space)), verbosity, fixedspace=true)
 
     ctm = leading_boundary(psi, alg, ctm)
     ctm2, = ctmrg_iter(psi, ctm, alg_fixed)
@@ -45,6 +41,7 @@ end
 
 @testset "Z2 symmetry ($T) - ($unitcell)" for (T, unitcell) in
                                               Iterators.product(scalartypes, unitcells)
+    Random.seed!(12345678)
     physical_space = Z2Space(0 => 1, 1 => 1)
     peps_space = Z2Space(0 => 2, 1 => 2)
     ctm_space = Z2Space(0 => 8, 1 => 8)
@@ -53,17 +50,11 @@ end
     psi = _make_symmetric(psi)
     ctm = CTMRGEnv(psi; Venv=ctm_space)
 
+    verbosity = 1
     alg = CTMRG(;
-        trscheme=truncdim(dim(ctm_space)), tol=1e-10, miniter=4, maxiter=100, verbosity=2
-    )
-    alg_fixed = CTMRG(;
-        trscheme=truncdim(dim(ctm_space)),
-        tol=1e-10,
-        miniter=4,
-        maxiter=100,
-        verbosity=2,
-        fixedspace=true,
+        trscheme=truncdim(dim(ctm_space)), tol=1e-10, miniter=4, maxiter=200, verbosity
     )
+    alg_fixed = CTMRG(; trscheme=truncdim(dim(ctm_space)), verbosity, fixedspace=true)
 
     ctm = leading_boundary(psi, alg, ctm)
     ctm2, = ctmrg_iter(psi, ctm, alg_fixed)

test/ctmrg/gradients.jl

@@ -1,24 +1,21 @@
 using Test
+using Random
 using PEPSKit
 using TensorKit
 using PEPSKit
 using Zygote
 using OptimKit
 using KrylovKit
+
 # Square lattice Heisenberg Hamiltonian
-function square_lattice_heisenberg(; Jx=-1.0, Jy=1.0, Jz=-1.0)
+function square_lattice_heisenberg(; Jx=-1, Jy=1, Jz=-1)
+    physical_space = ComplexSpace(2)
     T = ComplexF64
-    Vphys = ℂ^2
-    σx = TensorMap(T[0 1; 1 0], Vphys, Vphys)
-    σy = TensorMap(T[0 im; -im 0], Vphys, Vphys)
-    σz = TensorMap(T[1 0; 0 -1], Vphys, Vphys)
-
-    @tensor H[-1 -3; -2 -4] :=
-        Jx * σx[-1, -2] * σx[-3, -4] +
-        Jy * σy[-1, -2] * σy[-3, -4] +
-        Jz * σz[-1, -2] * σz[-3, -4]
-
-    return NLocalOperator{NearestNeighbor}(H)
+    σx = TensorMap(T[0 1; 1 0], physical_space, physical_space)
+    σy = TensorMap(T[0 im; -im 0], physical_space, physical_space)
+    σz = TensorMap(T[1 0; 0 -1], physical_space, physical_space)
+    H = (Jx * σx ⊗ σx) + (Jy * σy ⊗ σy) + (Jz * σz ⊗ σz)
+    return NLocalOperator{NearestNeighbor}(H / 4)
 end
 
 h = TensorMap(randn, Float64, ComplexSpace(2)^2, ComplexSpace(2)^2)
@@ -38,6 +35,7 @@ gradmodes = [nothing, GeomSum(; tol), ManualIter(; tol), KrylovKit.GMRES(; tol)]
 
 @testset "$alg_rrule" for alg_rrule in gradmodes
     # random point, random direction
+    Random.seed!(42039482038)
     dir = InfinitePEPS(2, χbond)
     psi = InfinitePEPS(2, χbond)
     env = leading_boundary(psi, boundary_alg, CTMRGEnv(psi; Venv=ℂ^χenv))

test/heisenberg.jl

@@ -1,4 +1,5 @@
 using Test
+using Random
 using PEPSKit
 using TensorKit
 using KrylovKit
@@ -34,6 +35,7 @@ opt_alg = PEPSOptimize(;
 )
 
 # initialize states
+Random.seed!(91283219347)
 psi_init = InfinitePEPS(2, χbond)
 env_init = leading_boundary(psi_init, ctm_alg, CTMRGEnv(psi_init; Venv=ComplexSpace(χenv)))