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
# test one-site DMRG
mps = FiniteMPS.random([2] * N, [D] * (N - 1),
dtype=dtype,
backend=backend)
dmrg = FiniteDMRG(mps, mpo)
one_site_energy = dmrg.run_one_site(num_sweeps=4, num_krylov_vecs=10)
np.testing.assert_allclose(one_site_energy, eta[0])
one_site_energy_no_sweeps = dmrg.run_one_site(num_sweeps=0,
num_krylov_vecs=10)
np.testing.assert_allclose(one_site_energy_no_sweeps, one_site_energy)
# test two-site DMRG
mps = FiniteMPS.random([2] * N, [D] * (N - 1),
dtype=dtype,
backend=backend)
dmrg = FiniteDMRG(mps, mpo)
two_site_energy = dmrg.run_two_site(max_bond_dim=D,
num_sweeps=4,
num_krylov_vecs=10)
np.testing.assert_allclose(two_site_energy, eta[0])
two_site_energy_no_sweeps = dmrg.run_two_site(max_bond_dim=D,
num_sweeps=0,
num_krylov_vecs=10)
np.testing.assert_allclose(two_site_energy_no_sweeps, two_site_energy)
def test_finite_DMRG_init(backend_dtype_values, N):
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_finite_DMRG_init(N, config, D):
backend = 'jax'
dtype = 'float64'
a = buildMPO()
mpo = FiniteMPO(a, backend=backend)
mps = FiniteMPS.random([4] * N, config, dtype=dtype, backend=backend)
ans1 = 500
ans2 = 100
sumtime = 0
while abs(2 * (ans1 - ans2) / (ans1 + ans2)) > 0.1 / 100:
print("Bond dim = {D}, Configuration = {c}".format(D=D, c=config))
bondarray.append(D)
start_time = time.time()
dmrg = FiniteDMRG(mps, mpo)
[mps, energy] = run_one_site(dmrg,
num_sweeps=1000,
num_krylov_vecs=15,
precision=prec,
delta=prec,
tol=prec,
verbose=0,
ndiag=1)
timearray.append(
jnp.around(float(time.time() - start_time) / 60, decimals=2))
print("{x} minutes".format(x=timearray[-1]))
sumtime += timearray[-1]
energy += 2
minarray.append(energy)
print("Minimum: {x}".format(x=minarray[-1]))
ans1 = ans2
ans2 = energy
precisionarray.append(
jnp.around(2 * (ans1 - ans2) / (ans1 + ans2) * 100, decimals=2))
print("Precision (%): {x}".format(x=precisionarray[-1]))
print("Number of iterations: {x}\n".format(x=numofitarray[-1]))
D *= 2
for i in range(N - 1):
config[i] *= 2
A = mps.tensors
B = jnp.zeros((1, 4, config[0]), dtype='float64')
B = jax.ops.index_update(B, jax.ops.index[:, :, :A[0].shape[2]], A[0])
A[0] = B
for i in range(1, N - 1):
B = jnp.zeros((config[i - 1], 4, config[i]), dtype='float64')
B = jax.ops.index_update(
B, jax.ops.index[:A[i].shape[0], :, :A[i].shape[2]], A[i])
A[i] = B
B = jnp.zeros((config[N - 2], 4, 1), dtype='float64')
B = jax.ops.index_update(B, jax.ops.index[:A[N - 1].shape[0], :, :],
A[N - 1])
A[N - 1] = B
mps.tensors = A
print("")
