test

Author: maartenvd

src/algorithms/expval.jl

@@ -128,6 +128,13 @@ function expectation_value(
     return dot(ψ, H, ψ, envs) / dot(ψ, ψ)
 end
 
+function expectation_value(
+        ψ::WindowMPS, H::WindowMPOHamiltonian,
+        envs::AbstractMPSEnvironments = environments(ψ, H)
+    )
+    return dot(ψ, H, ψ, envs) / dot(ψ, ψ)
+end
+
 function expectation_value(
         ψ::InfiniteMPS, H::InfiniteMPOHamiltonian,
         envs::AbstractMPSEnvironments = environments(ψ, H)

src/operators/windowhamiltonian.jl

@@ -34,3 +34,8 @@ Base.copy(h::WindowMPOHamiltonian) = WindowMPOHamiltonian(copy(h.left_ham), copy
 for fun in (:(Base.:+), :(Base.:-), :(Base.:*))
     @eval $fun(a::WindowMPOHamiltonian,b::WindowMPOHamiltonian) = WindowMPOHamiltonian($fun(a.left_ham,b.left_ham),$fun(a.finite_ham,b.finite_ham),$fun(a.right_ham,b.right_ham))
 end
+
+TensorKit.dot(
+        bra::WindowMPS, H::WindowMPOHamiltonian, ket::WindowMPS = bra,
+        envs = environments(bra, H, ket)
+        ) = dot(bra.window, H.finite_ham, ket.window,envs)

test/algorithms/dynamical_dmrg.jl

@@ -12,7 +12,7 @@ using TensorKit: ℙ
 
 verbosity_conv = 1
 
-@testset "Dynamical DMRG" verbose = true begin
+@testset "Dynamical DMRG FiniteMPS" verbose = true begin
     L = 10
     H = force_planar(-transverse_field_ising(; L, g = -4))
     gs, = find_groundstate(FiniteMPS(L, ℙ^2, ℙ^10), H; verbosity = verbosity_conv)
@@ -32,3 +32,32 @@ verbosity_conv = 1
         @test data ≈ predicted atol = 1.0e-8
     end
 end
+
+
+@testset "Dynamical DMRG WindowMPS" verbose = true begin
+    N = 20
+
+    H = transverse_field_ising(g = -4)
+    Ω = InfiniteMPS(ComplexSpace(2),ComplexSpace(20))
+
+    (Ω,_) = find_groundstate(Ω, H, VUMPS(verbosity = verbosity_conv))
+    XΩ = WindowMPS(Ω,N)
+    H_w = WindowMPOHamiltonian(H,1:N)
+    
+
+    gs_en =  expectation_value(XΩ, H_w)
+    vals = range(gs_en - 1.0,gs_en+1.0, length=5)
+    eta = 0.3im
+    predicted = [1 / (v + eta - gs_en) for v in vals]
+
+
+    @testset "Flavour $f" for f in (Jeckelmann(), NaiveInvert())
+        alg = DynamicalDMRG(; flavour = f, verbosity = 0, tol = 1.0e-8)
+        data = map(vals) do v
+            result, = propagator(XΩ, v + eta, H_w, alg)
+            return result
+        end
+        @test data ≈ predicted atol = 1.0e-8
+    end
+end
+