def test_BaseDMRG_raises():
numpy_backend = backend_factory.get_backend('numpy')
pytorch_backend = backend_factory.get_backend('pytorch')
dtype = np.float64
N = 10
D = 10
Jz = np.ones(N - 1)
Jxy = np.ones(N - 1)
Bz = np.zeros(N)
mpo = FiniteXXZ(Jz, Jxy, Bz, dtype=dtype, backend=numpy_backend)
mps = FiniteMPS.random(
[2] * (N - 1), [D] * (N - 2), dtype=dtype, backend=numpy_backend)
with pytest.raises(ValueError):
BaseDMRG(mps, mpo, numpy_backend.ones((1, 1, 1), dtype=dtype),
numpy_backend.ones((1, 1, 1), dtype=dtype), 'name')
mpo = FiniteXXZ(Jz, Jxy, Bz, dtype=np.float64, backend=numpy_backend)
mps = FiniteMPS.random(
[2] * N, [D] * (N - 1), dtype=np.float32, backend=numpy_backend)
with pytest.raises(
TypeError,
match="mps.dtype = {} is different from "
"mpo.dtype = {}".format(mps.dtype, mpo.dtype)):
BaseDMRG(mps, mpo, numpy_backend.ones((1, 1, 1), dtype=dtype),
numpy_backend.ones((1, 1, 1), dtype=dtype), 'name')
mpo = FiniteXXZ(Jz, Jxy, Bz, dtype=np.float64, backend=numpy_backend)
mps = FiniteMPS.random(
[2] * N, [D] * (N - 1), dtype=np.float64, backend=pytorch_backend)
with pytest.raises(TypeError, match="mps and mpo use different backends."):
BaseDMRG(mps, mpo, numpy_backend.ones((1, 1, 1), dtype=dtype),
numpy_backend.ones((1, 1, 1), dtype=dtype), 'name')
def test_finite_DMRG_outstream(backend_dtype_values, capsys):
np.random.seed(16)
N = 6
backend = backend_dtype_values[0]
dtype = backend_dtype_values[1]
mpo = FiniteXXZ(
Jz=np.ones(N - 1),
Jxy=np.ones(N - 1),
Bz=np.zeros(N),
dtype=dtype,
backend=backend)
D = 32
mps = FiniteMPS.random([2] * N, [D] * (N - 1), dtype=dtype, backend=backend)
dmrg = FiniteDMRG(mps, mpo)
num_sweeps = 2
dmrg.run_one_site(
num_sweeps=num_sweeps, num_krylov_vecs=10, verbose=2, precision=1E-100)
out, _ = capsys.readouterr()
out = out.split('\n')
act = [o[:28] + '\n' for o in out]
act = ''.join(act[0:num_sweeps * (2 * N - 2)])
exp = ''.join([
f"SS-DMRG sweep={n}/{num_sweeps}, site={m}/{N}:\n"
for n in range(1, num_sweeps + 1)
for m in [0, 1, 2, 3, 4, 5, 4, 3, 2, 1]
])
assert act == exp
def test_BaseDMRG_position(backend_dtype_values):
backend = backend_factory.get_backend(backend_dtype_values[0])
dtype = backend_dtype_values[1]
N = 10
D = 10
Jz = np.ones(N - 1)
Jxy = np.ones(N - 1)
Bz = np.zeros(N)
mpo = FiniteXXZ(Jz, Jxy, Bz, dtype=dtype, backend=backend)
mps = FiniteMPS.random([2] * N, [D] * (N - 1), dtype=dtype, backend=backend)
dmrg = BaseDMRG(mps, mpo, np.ones((1, 1, 1), dtype=dtype),
np.ones((1, 1, 1), dtype=dtype), 'name')
dmrg.position(N - 1)
np.testing.assert_allclose(np.arange(N), sorted(list(dmrg.left_envs.keys())))
np.testing.assert_allclose([N - 1], list(dmrg.right_envs.keys()))
assert dmrg.mps.center_position == N - 1
dmrg.position(0)
np.testing.assert_allclose([0], list(dmrg.left_envs.keys()))
np.testing.assert_allclose(np.arange(N), sorted(list(dmrg.right_envs.keys())))
assert dmrg.mps.center_position == 0
with pytest.raises(IndexError, match="site > length of mps"):
dmrg.position(N)
with pytest.raises(IndexError, match="site < 0"):
dmrg.position(-1)
def test_BaseDMRG_init(backend_dtype_values):
backend = backend_factory.get_backend(backend_dtype_values[0])
dtype = backend_dtype_values[1]
N = 10
D = 10
Jz = np.ones(N - 1)
Jxy = np.ones(N - 1)
Bz = np.zeros(N)
mpo = FiniteXXZ(Jz, Jxy, Bz, dtype=dtype, backend=backend)
mps = FiniteMPS.random([2] * N, [D] * (N - 1), dtype=dtype, backend=backend)
dmrg = BaseDMRG(mps, mpo, np.ones((1, 1, 1), dtype=dtype),
np.ones((1, 1, 1), dtype=dtype), 'name')
assert dmrg.name == 'name'
assert dmrg.backend is backend
def test_finite_DMRG_init(backend_dtype_values, N):
np.random.seed(16)
backend = backend_dtype_values[0]
dtype = backend_dtype_values[1]
H = get_XXZ_Hamiltonian(N, 1, 1, 1)
eta, _ = np.linalg.eigh(H)
mpo = FiniteXXZ(
Jz=np.ones(N - 1),
Jxy=np.ones(N - 1),
Bz=np.zeros(N),
dtype=dtype,
backend=backend)
D = 32
mps = FiniteMPS.random([2] * N, [D] * (N - 1), dtype=dtype, backend=backend)
dmrg = FiniteDMRG(mps, mpo)
energy = dmrg.run_one_site(num_sweeps=4, num_krylov_vecs=10)
np.testing.assert_allclose(energy, eta[0])
def test_compute_envs(backend_dtype_values):
backend = backend_factory.get_backend(backend_dtype_values[0])
dtype = backend_dtype_values[1]
N = 10
D = 10
Jz = np.ones(N - 1)
Jxy = np.ones(N - 1)
Bz = np.zeros(N)
mpo = FiniteXXZ(Jz, Jxy, Bz, dtype=dtype, backend=backend)
mps = FiniteMPS.random([2] * N, [D] * (N - 1), dtype=dtype, backend=backend)
dmrg = BaseDMRG(mps, mpo, np.ones((1, 1, 1), dtype=dtype),
np.ones((1, 1, 1), dtype=dtype), 'name')
dmrg.position(5)
dmrg.compute_left_envs()
dmrg.compute_right_envs()
np.testing.assert_allclose([0, 1, 2, 3, 4, 5],
sorted(list(dmrg.left_envs.keys())))
np.testing.assert_allclose([5, 6, 7, 8, 9],
sorted(list(dmrg.right_envs.keys())))
def test_BaseDMRG_raises_2():
backend = 'numpy'
backend_obj = backend_factory.get_backend(backend)
dtype = np.float64
N = 10
D = 10
Jz = np.ones(N - 1)
Jxy = np.ones(N - 1)
Bz = np.zeros(N)
mpo = FiniteXXZ(Jz, Jxy, Bz, dtype=dtype, backend=backend)
tensors = [np.random.randn(1, 2, D)] + [
np.random.randn(D, 2, D) for _ in range(N - 2)
] + [np.random.randn(D, 2, 1)]
mps = FiniteMPS(
tensors, center_position=None, canonicalize=False, backend=backend)
with pytest.raises(
ValueError,
match="Found mps in non-canonical form. Please canonicalize mps."):
BaseDMRG(mps, mpo, backend_obj.ones((1, 1, 1), dtype=dtype),
backend_obj.ones((1, 1, 1), dtype=dtype), 'name')