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Merged
lkdvos merged 9 commits into from
Sep 26, 2026
Merged

Tests for Hamiltonian derivatives and long-range models #493

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5923e9a
add long-range hamiltonians to test setup
borisdevos Jul 31, 2026
79ed55b
test long-range hamiltonians ground state search
borisdevos Jul 31, 2026
77f9ac2
clarifying comment
borisdevos Aug 7, 2026
0fa2107
explicitly test properties of derivative operators
borisdevos Jul 31, 2026
50f1f9d
move derivatives test to test/hamiltonian/ to match reorganized test …
lkdvos Sep 23, 2026
848af94
discover and add to the changelog
borisdevos Jul 31, 2026
196b1db
Bump BlockTensorKit compat to 0.3.19
lkdvos Sep 23, 2026
874fbae
Fold long-range Hamiltonian coverage into groundstate.jl model/algori…
lkdvos Sep 24, 2026
4b52e55
Simplify groundstate.jl comments and drop redundant DMRG-reuse check
lkdvos Sep 26, 2026
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9 changes: 9 additions & 0 deletions docs/src/changelog.md
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Original file line number Diff line number Diff line change
Expand Up @@ -123,6 +123,15 @@ When releasing a new version, move the "Unreleased" changes to a new version sec
return value regardless, so this adds no extra cost. ([#509](https://github.com/QuantumKitHub/MPSKit.jl/pull/509))
- `make_time_mpo` with `TaylorCluster` on a Hamiltonian whose virtual bond dimension varies along
the chain are now correctly handled. ([#511](https://github.com/QuantumKitHub/MPSKit.jl/pull/511))
- The converting constructor of `JordanMPO_AC_Hamiltonian` assigned the "finished" block `E` to
the "ending" field `B` whenever `B` was absent, raising a `convert` `MethodError` from deep
inside `AC_hamiltonian`. This is reached when an `MPOHamiltonian` whose scalartype differs from
the state's has an on-site term on a site where no interaction ends, such as a long-range one.
([#493](https://github.com/QuantumKitHub/MPSKit.jl/pull/493))
- Raised the `BlockTensorKit` compat lower bound to 0.3.19, which fixes a silent data-loss bug: with
`TensorKit` 0.17.2 and `BlockTensorKit` <= 0.3.18, a permuted contraction into a sparse block
tensor (e.g. an environment sweep near a `FiniteMPS` chain boundary when the operator and state
have different `scalartype`s) could silently drop data instead of erroring.

### Performance

Expand Down
280 changes: 155 additions & 125 deletions test/groundstate/groundstate.jl
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Expand Up @@ -20,132 +20,152 @@ verbosity_conv = 1
D = 6
L = 10

H = force_planar(transverse_field_ising(; g, L))
models = [
"nearest-neighbour" => force_planar(transverse_field_ising(; g, L)),
"long-range, real scalartype" => force_planar(long_range_ising(Float64; g, L)),
]

@testset "DMRG" begin
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^D)
v₀ = variance(ψ₀, H)
@testset "$name" for (name, H) in models
@testset "DMRG" begin
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^D)
v₀ = variance(ψ₀, H)

# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG(; verbosity = verbosity_full, maxiter = 2)
)
# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG(; verbosity = verbosity_full, maxiter = 2)
)

ψ, envs, δ = find_groundstate(
ψ, H, DMRG(; verbosity = verbosity_conv, maxiter = 10), envs
)
v = variance(ψ, H)
ψ, envs, δ = find_groundstate(
ψ, H, DMRG(; verbosity = verbosity_conv, maxiter = 10), envs
)
v = variance(ψ, H)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
end

# the algorithm object carries no scratch space of its own - the sweep's allocator is
# obtained per solve - so re-using one across solves has to reproduce the answer
alg = DMRG(; verbosity = verbosity_conv, maxiter = 10)
ψ1, = find_groundstate(ψ₀, H, alg)
ψ2, = find_groundstate(ψ₀, H, alg)
@test expectation_value(ψ1, H) ≈ expectation_value(ψ2, H) atol = 1.0e-10
end
@testset "DMRG2" begin
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^D)
v₀ = variance(ψ₀, H)
trunc = truncrank(floor(Int, D * 1.5))
# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG2(; verbosity = verbosity_full, maxiter = 2, trunc)
)

@testset "DMRG2" begin
ψ₀ = FiniteMPS(randn, ComplexF64, 10, ℙ^2, ℙ^D)
v₀ = variance(ψ₀, H)
trunc = truncrank(floor(Int, D * 1.5))
# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG2(; verbosity = verbosity_full, maxiter = 2, trunc)
)
ψ, envs, δ = find_groundstate(
ψ, H, DMRG2(; verbosity = verbosity_conv, maxiter = 10, trunc), envs
)
v = variance(ψ, H)

ψ, envs, δ = find_groundstate(
ψ, H, DMRG2(; verbosity = verbosity_conv, maxiter = 10, trunc), envs
)
v = variance(ψ, H)
# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
end

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
end
@testset "CBEDMRG" begin
# start from a small bond so the bond expansion is exercised
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^(D ÷ 2))
v₀ = variance(ψ₀, H)
expand = OptimalExpand(; trunc = truncrank(D ÷ 2))
trunc = truncrank(D)

@testset "CBEDMRG" begin
# start from a small bond so the bond expansion is exercised
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^(D ÷ 2))
v₀ = variance(ψ₀, H)
expand = OptimalExpand(; trunc = truncrank(D ÷ 2))
trunc = truncrank(D)
# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG(; verbosity = verbosity_full, maxiter = 2, alg_expand = expand, trunc)
)

# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG(; verbosity = verbosity_full, maxiter = 2, alg_expand = expand, trunc)
)
ψ, envs, δ = find_groundstate(
ψ, H, DMRG(; verbosity = verbosity_conv, maxiter = 10, alg_expand = expand, trunc), envs
)
v = variance(ψ, H)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
# the bond should have grown to the truncation target
@test dim(left_virtualspace(ψ, L ÷ 2)) == D
end

ψ, envs, δ = find_groundstate(
ψ, H, DMRG(; verbosity = verbosity_conv, maxiter = 10, alg_expand = expand, trunc), envs
)
v = variance(ψ, H)
@testset "CBEDMRG (SketchedExpand)" begin
# randomized bond expansion at single-site cost. The sketch is redrawn every sweep, so an
# aggressive expansion (a large fraction of the bond) keeps the single-site Galerkin error
# noisy; a gentle per-sweep increment lets it converge like the deterministic expanders.
Random.seed!(1234)
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^(D ÷ 2))
v₀ = variance(ψ₀, H)
expand = SketchedExpand(; trunc = truncrank(2), oversampling = 4)
trunc = truncrank(D)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
# the bond should have grown to the truncation target
@test dim(left_virtualspace(ψ, L ÷ 2)) == D
end
# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG(; verbosity = verbosity_full, maxiter = 2, alg_expand = expand, trunc)
)

@testset "CBEDMRG (SketchedExpand)" begin
# randomized bond expansion at single-site cost. The sketch is redrawn every sweep, so an
# aggressive expansion (a large fraction of the bond) keeps the single-site Galerkin error
# noisy; a gentle per-sweep increment lets it converge like the deterministic expanders.
Random.seed!(1234)
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^(D ÷ 2))
v₀ = variance(ψ₀, H)
expand = SketchedExpand(; trunc = truncrank(2), oversampling = 4)
trunc = truncrank(D)
ψ, envs, δ = find_groundstate(
ψ, H, DMRG(; verbosity = verbosity_conv, maxiter = 15, alg_expand = expand, trunc), envs
)
v = variance(ψ, H)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
# the bond should have grown to the truncation target
@test dim(left_virtualspace(ψ, L ÷ 2)) == D
end

# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG(; verbosity = verbosity_full, maxiter = 2, alg_expand = expand, trunc)
)
@testset "DMRG3S" begin
# start from a small bond so the post-expansion is exercised, mirroring CBEDMRG above
Random.seed!(1234)
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^(D ÷ 2))
v₀ = variance(ψ₀, H)
alg_gauge = DMRG3S(0.1, ExponentialDecay(0.7)) # TODO: match final constructor API
trunc = truncrank(D)

ψ, envs, δ = find_groundstate(
ψ, H, DMRG(; verbosity = verbosity_conv, maxiter = 15, alg_expand = expand, trunc), envs
)
v = variance(ψ, H)
# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG(; verbosity = verbosity_full, maxiter = 2, alg_gauge, trunc)
)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
# the bond should have grown to the truncation target
@test dim(left_virtualspace(ψ, L ÷ 2)) == D
end
ψ, envs, δ = find_groundstate(
ψ, H, DMRG(; verbosity = verbosity_conv, maxiter = 10, alg_gauge, trunc), envs
)
v = variance(ψ, H)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
# the bond should have grown to the truncation target
@test dim(left_virtualspace(ψ, L ÷ 2)) == D
end

@testset "DMRG3S" begin
# start from a small bond so the post-expansion is exercised, mirroring CBEDMRG above
Random.seed!(1234)
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^(D ÷ 2))
v₀ = variance(ψ₀, H)
alg_gauge = DMRG3S(0.1, ExponentialDecay(0.7)) # TODO: match final constructor API
trunc = truncrank(D)
@testset "GradientGrassmann" begin
ψ₀ = FiniteMPS(randn, ComplexF64, L, ℙ^2, ℙ^D)
v₀ = variance(ψ₀, H)

# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, DMRG(; verbosity = verbosity_full, maxiter = 2, alg_gauge, trunc)
)
# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, GradientGrassmann(; verbosity = verbosity_full, maxiter = 2)
)

ψ, envs, δ = find_groundstate(
ψ, H, DMRG(; verbosity = verbosity_conv, maxiter = 10, alg_gauge, trunc), envs
)
v = variance(ψ, H)
# an explicit `tol` keeps the optimizer from overshooting past the point where the
# gradient is floating-point noise: pushed further, the CG line search can hit a
# 0/0 in its step-size formula and feed a NaN tangent into the Grassmann retraction
ψ, envs, δ = find_groundstate(
ψ, H, GradientGrassmann(; tol, verbosity = verbosity_conv, maxiter = 50), envs
)
v = variance(ψ, H)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
# the bond should have grown to the truncation target
@test dim(left_virtualspace(ψ, L ÷ 2)) == D
# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀ && v < 1.0e-2
end
end

fast_tests || @testset "DMRG3S escapes local minimum (Hubig et al. 2015, Sec. VII A)" begin
Expand Down Expand Up @@ -173,25 +193,6 @@ verbosity_conv = 1
@test E_escape < E_stuck - 1.0
@test isapprox(E_escape, -8.6824724; atol = 1.0e-4)
end

@testset "GradientGrassmann" begin
ψ₀ = FiniteMPS(randn, ComplexF64, 10, ℙ^2, ℙ^D)
v₀ = variance(ψ₀, H)

# test logging
ψ, envs, δ = find_groundstate(
ψ₀, H, GradientGrassmann(; verbosity = verbosity_full, maxiter = 2)
)

ψ, envs, δ = find_groundstate(
ψ, H, GradientGrassmann(; verbosity = verbosity_conv, maxiter = 50), envs
)
v = variance(ψ, H)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀ && v < 1.0e-2
end
end

@testset "InfiniteMPS ground state" verbose = true begin
Expand All @@ -206,6 +207,7 @@ end
H_ref_realT = force_planar(transverse_field_ising(Float64; g))
@test variance(ψ, H_ref_realT) ≈ v₀ atol = 1.0e-10


@testset "VUMPS (unit cell $unit_cell_size, $schedname)" for unit_cell_size in [1, 3],
(schedname, scheduler) in SCHEDULERS

Expand All @@ -227,6 +229,20 @@ end
@test v < 1.0e-2
end

@testset "VUMPS (long-range, real scalartype)" begin
H = force_planar(long_range_ising_infinite(Float64; g, L = 3))
ψ₀ = InfiniteMPS(fill(ℙ^2, 3), fill(ℙ^D, 3))
v₀ = variance(ψ₀, H)

ψ′, envs, δ = find_groundstate(ψ₀, H, VUMPS(; tol, verbosity = verbosity_conv, maxiter = 20))
v = variance(ψ′, H, envs)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
end

@testset "IDMRG" for unit_cell_size in [1, 3]
ψ = unit_cell_size == 1 ? InfiniteMPS(ℙ^2, ℙ^D) : repeat(ψ, unit_cell_size)
H = repeat(H_ref, unit_cell_size)
Expand All @@ -245,6 +261,20 @@ end
@test v < 1.0e-2
end

@testset "IDMRG (long-range, real scalartype)" begin
H = force_planar(long_range_ising_infinite(Float64; g, L = 3))
ψ₀ = InfiniteMPS(fill(ℙ^2, 3), fill(ℙ^D, 3))
v₀ = variance(ψ₀, H)

ψ, envs, δ = find_groundstate(ψ₀, H, IDMRG(; tol, verbosity = verbosity_conv, maxiter = 20))
v = variance(ψ, H, envs)

# test using low variance
@test sum(δ) ≈ 0 atol = 1.0e-3
@test v < v₀
@test v < 1.0e-2
end

@testset "IDMRG2" begin
ψ = repeat(InfiniteMPS(ℙ^2, ℙ^D), 2)
H = repeat(H_ref, 2)
Expand Down
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