def test_find_connections(self):
expr = [ExpressionR(),
c_dag("up", 0) * c("dn", 1),
c_dag("up", 0) * c("dn", 1) + c_dag("dn", 0) * c("dn", 1),
c_dag("dn", 1) * c_dag("up", 1),
n("up", 2)
]
sp, melem = make_space_partition(self.Hop, self.hs, False)
op = LOperatorR(sum(expr[1:]), self.hs)
conns = sp.find_connections(op, self.hs)
conns_ref = set()
in_state = np.zeros((sp.dim,), dtype=float)
for i in range(sp.dim):
in_state[i] = 1.0
out_state = op * in_state
for f, a in enumerate(out_state):
if abs(a) > 1e-10:
conns_ref.add((sp[i], sp[f]))
in_state[i] = 0.0
self.assertEqual(conns, conns_ref)
for expr1, expr2 in product(expr, expr):
conns1 = sp.find_connections(LOperatorR(expr1, self.hs), self.hs)
conns2 = sp.find_connections(LOperatorR(expr2, self.hs), self.hs)
conns = sp.find_connections(LOperatorR(expr1 + expr2, self.hs),
self.hs)
self.assertEqual(conns, conns1.union(conns2))
def test_HilbertSpace(self):
indices = [("dn", 0), ("dn", 1), ("up", 0), ("up", 1)]
hs = HilbertSpace([make_space_fermion(*ind) for ind in indices])
for ind in indices:
c_op = LOperatorR(c(*ind), hs)
assert_equal(make_matrix(c_op, hs), self.c_mat(hs, *ind))
c_dag_op = LOperatorR(c_dag(*ind), hs)
assert_equal(make_matrix(c_dag_op, hs), self.c_dag_mat(hs, *ind))
H1 = 1.0 * (c_dag("up", 0) * c("up", 1) + c_dag("up", 1) * c("up", 0))
H1 += 2.0 * (c_dag("dn", 0) * c("dn", 1) + c_dag("dn", 1) * c("dn", 0))
H1op = LOperatorR(H1, hs)
ref1 = 1.0 * (self.c_dag_mat(hs, "up", 0) @ self.c_mat(hs, "up", 1) +
self.c_dag_mat(hs, "up", 1) @ self.c_mat(hs, "up", 0))
ref1 += 2.0 * (self.c_dag_mat(hs, "dn", 0) @ self.c_mat(hs, "dn", 1) +
self.c_dag_mat(hs, "dn", 1) @ self.c_mat(hs, "dn", 0))
assert_equal(make_matrix(H1op, hs), ref1)
H2 = 1.0j * (c_dag("up", 0) * c("up", 1) + c_dag("up", 1) * c("up", 0))
H2 += 2.0 * (c_dag("dn", 0) * c("dn", 1) + c_dag("dn", 1) * c("dn", 0))
H2op = LOperatorC(H2, hs)
ref2 = 1.0j * (self.c_dag_mat(hs, "up", 0) @ self.c_mat(hs, "up", 1) +
self.c_dag_mat(hs, "up", 1) @ self.c_mat(hs, "up", 0))
ref2 += 2.0 * (self.c_dag_mat(hs, "dn", 0) @ self.c_mat(hs, "dn", 1) +
self.c_dag_mat(hs, "dn", 1) @ self.c_mat(hs, "dn", 0))
assert_equal(make_matrix(H2op, hs), ref2)
def test_loperator(self):
out = np.zeros((6,), dtype=float)
dst = self.mapper(out)
# Spin flips
Hop = LOperatorR(self.Hex, self.hs)
in1 = np.array([1, 1, 1, 1, 1, 1], dtype=float)
src = self.mapper(in1)
Hop(src, dst)
assert_equal(out, np.array([0, 0, 2, 2, 0, 0], dtype=float))
in2 = np.array([1, 1, 1, -1, 1, 1], dtype=float)
src = self.mapper(in2)
Hop(src, dst)
assert_equal(out, np.array([0, 0, -2, 2, 0, 0], dtype=float))
# Pair hops
Hop = LOperatorR(self.Hp, self.hs)
src = self.mapper(in1)
Hop(src, dst)
assert_equal(out, np.array([0, 2, 0, 0, 2, 0], dtype=float))
in2 = np.array([1, 1, 1, 1, -1, 1], dtype=float)
src = self.mapper(in2)
Hop(src, dst)
assert_equal(out, np.array([0, -2, 0, 0, 2, 0], dtype=float))
def test_compositions(self):
mapper = BasisMapper([], self.hs, 0)
self.assertEqual(len(mapper), 1)
self.assertEqual(mapper.map[0], 0)
O_list = [LOperatorR(c_dag("dn", 1), self.hs),
LOperatorR(c_dag("dn", 2), self.hs),
LOperatorR(c_dag("up", 1), self.hs),
LOperatorR(c_dag("up", 2), self.hs)]
mapper_N0 = BasisMapper(O_list, self.hs, 0)
self.assertEqual(len(mapper_N0), 1)
self.assertEqual(mapper_N0.map[0], 0)
mapper_N2 = BasisMapper(O_list, self.hs, 2)
self.assert_equal_dicts_up_to_value_permutation(mapper_N2.map,
self.mapping)
O_list_complex = [LOperatorC(1j * c_dag("dn", 1), self.hs),
LOperatorC(1j * c_dag("dn", 2), self.hs),
LOperatorC(1j * c_dag("up", 1), self.hs),
LOperatorC(1j * c_dag("up", 2), self.hs)]
mapper_N2 = BasisMapper(O_list_complex, self.hs, 2)
self.assert_equal_dicts_up_to_value_permutation(mapper_N2.map,
self.mapping)
def test_compositions_bosons(self):
hs = HilbertSpace(a_dag(1) + a_dag(2) + a_dag(3) + a_dag(4), 4)
O_list = [LOperatorR(a_dag(1), hs),
LOperatorR(a_dag(2), hs),
LOperatorR(a_dag(3), hs),
LOperatorR(a_dag(4), hs)]
map_size_ref = [1, 4, 10, 20, 35, 56, 84, 120, 165, 220]
for N in range(10):
mapper = BasisMapper(O_list, hs, N)
self.assertEqual(len(mapper), map_size_ref[N])
def test_LOperatorR(self):
expr1 = 3 * c_dag("dn")
expr2 = 3 * c("up")
expr = 2 * expr1 - 2 * expr2
hs = HilbertSpace(expr)
lop1 = LOperatorR(expr1, hs)
lop2 = LOperatorR(expr2, hs)
lop = LOperatorR(expr, hs)
src = np.array([1, 1, 1, 1])
dst_real = np.zeros((4, ), dtype=float)
dst_complex = np.zeros((4, ), dtype=complex)
assert_equal(lop1 * src, np.array([0, 3, 0, 3]))
lop1(src, dst_real)
assert_equal(dst_real, np.array([0, 3, 0, 3]))
lop1(src, dst_complex)
assert_equal(dst_complex, np.array([0, 3, 0, 3], dtype=complex))
assert_equal(lop2 * src, np.array([3, -3, 0, 0]))
lop2(src, dst_real)
assert_equal(dst_real, np.array([3, -3, 0, 0]))
lop2(src, dst_complex)
assert_equal(dst_complex, np.array([3, -3, 0, 0], dtype=complex))
assert_equal(lop * src, np.array([-6, 12, 0, 6]))
lop(src, dst_real)
assert_equal(dst_real, np.array([-6, 12, 0, 6]))
lop(src, dst_complex)
assert_equal(dst_complex, np.array([-6, 12, 0, 6], dtype=complex))
src_complex = 1j * np.array([1, 1, 1, 1])
assert_equal(lop * src_complex, np.array([-6j, 12j, 0, 6j]))
lop(src_complex, dst_complex)
assert_equal(dst_complex, np.array([-6j, 12j, 0, 6j]))
with self.assertRaisesRegex(
RuntimeError, "^State vector must be a 1-dimensional array$"):
lop * np.zeros((3, 3, 3))
with self.assertRaisesRegex(
RuntimeError,
"^Source state vector must be a 1-dimensional array$"):
lop(np.zeros((3, 3, 3)), np.zeros((5, )))
with self.assertRaisesRegex(
RuntimeError,
"^Destination state vector must be a 1-dimensional array$"):
lop(np.zeros((5, )), np.zeros((3, 3, 3)))
def test_basis_state_indices(self):
indices = [("dn", 0), ("dn", 1), ("up", 0), ("up", 1)]
hs = HilbertSpace([make_space_fermion(*ind) for ind in indices])
# Basis of the N=2 sector
basis_state_indices = [3, 5, 6, 9, 10, 12]
H1 = 1.0 * (c_dag("up", 0) * c("up", 1) + c_dag("up", 1) * c("up", 0))
H1 += 2.0 * (c_dag("dn", 0) * c("dn", 1) + c_dag("dn", 1) * c("dn", 0))
H1op = LOperatorR(H1, hs)
ref1 = 1.0 * (self.c_dag_mat(hs, "up", 0) @ self.c_mat(hs, "up", 1) +
self.c_dag_mat(hs, "up", 1) @ self.c_mat(hs, "up", 0))
ref1 += 2.0 * (self.c_dag_mat(hs, "dn", 0) @ self.c_mat(hs, "dn", 1) +
self.c_dag_mat(hs, "dn", 1) @ self.c_mat(hs, "dn", 0))
ref1 = ref1[basis_state_indices, :][:, basis_state_indices]
assert_equal(make_matrix(H1op, basis_state_indices), ref1)
H2 = 1.0j * (c_dag("up", 0) * c("up", 1) + c_dag("up", 1) * c("up", 0))
H2 += 2.0 * (c_dag("dn", 0) * c("dn", 1) + c_dag("dn", 1) * c("dn", 0))
H2op = LOperatorC(H2, hs)
ref2 = 1.0j * (self.c_dag_mat(hs, "up", 0) @ self.c_mat(hs, "up", 1) +
self.c_dag_mat(hs, "up", 1) @ self.c_mat(hs, "up", 0))
ref2 += 2.0 * (self.c_dag_mat(hs, "dn", 0) @ self.c_mat(hs, "dn", 1) +
self.c_dag_mat(hs, "dn", 1) @ self.c_mat(hs, "dn", 0))
ref2 = ref2[basis_state_indices, :][:, basis_state_indices]
assert_equal(make_matrix(H2op, basis_state_indices), ref2)
def test_overload_selection(self):
expr = 6 * c_dag("dn") - 6 * c("up")
hs = HilbertSpace(expr)
lop = LOperatorR(expr, hs)
src_int = np.array([1, 1, 1, 1], dtype=int)
src_real = np.array([1, 1, 1, 1], dtype=float)
src_complex = np.array([1, 1, 1, 1], dtype=complex)
ref_real = np.array([-6, 12, 0, 6], dtype=float)
ref_complex = np.array([-6, 12, 0, 6], dtype=complex)
self.assertEqual((lop * src_int).dtype, np.float64)
self.assertEqual((lop * src_real).dtype, np.float64)
self.assertEqual((lop * src_complex).dtype, np.complex128)
dst_int = np.zeros(4, dtype=int)
dst_real = np.zeros(4, dtype=float)
dst_complex = np.zeros(4, dtype=complex)
self.assertRaises(TypeError, lop, src_int, dst_int)
self.assertRaises(TypeError, lop, src_real, dst_int)
self.assertRaises(TypeError, lop, src_complex, dst_int)
self.assertRaises(TypeError, lop, src_complex, dst_real)
lop(src_int, dst_real)
assert_equal(dst_real, ref_real)
lop(src_int, dst_complex)
assert_equal(dst_complex, ref_complex)
lop(src_real, dst_real)
assert_equal(dst_real, ref_real)
lop(src_real, dst_complex)
assert_equal(dst_complex, ref_complex)
lop(src_complex, dst_complex)
assert_equal(dst_complex, ref_complex)
expr = 6j * c_dag("dn") - 6j * c("up")
hs = HilbertSpace(expr)
lop = LOperatorC(expr, hs)
self.assertEqual((lop * src_int).dtype, np.complex128)
self.assertEqual((lop * src_real).dtype, np.complex128)
self.assertEqual((lop * src_complex).dtype, np.complex128)
ref_complex = np.array([-6j, 12j, 0, 6j], dtype=complex)
self.assertRaises(TypeError, lop, src_int, dst_int)
self.assertRaises(TypeError, lop, src_real, dst_int)
self.assertRaises(TypeError, lop, src_complex, dst_int)
self.assertRaises(TypeError, lop, src_int, dst_real)
self.assertRaises(TypeError, lop, src_real, dst_real)
self.assertRaises(TypeError, lop, src_complex, dst_real)
lop(src_int, dst_complex)
assert_equal(dst_complex, ref_complex)
lop(src_real, dst_complex)
assert_equal(dst_complex, ref_complex)
lop(src_complex, dst_complex)
assert_equal(dst_complex, ref_complex)
def test_empty(self):
expr0 = ExpressionR()
hs = HilbertSpace(expr0)
lop = LOperatorR(expr0, hs)
sv = np.array([], dtype=float)
assert_equal(lop * sv, sv)
dst = np.array([], dtype=float)
lop(sv, dst)
assert_equal(sv, dst)
def test_merge_subspaces(self):
sp, melem = make_space_partition(self.Hop, self.hs, False)
Cd, C, all_ops = [], [], []
for spin in ("dn", "up"):
for o in range(self.n_orbs):
Cd.append(LOperatorR(c_dag(spin, o), self.hs))
C.append(LOperatorR(c(spin, o), self.hs))
all_ops.append(Cd[-1])
all_ops.append(C[-1])
sp.merge_subspaces(Cd[-1], C[-1], self.hs)
# Calculated classification of states
v_cl = [set() for _ in range(sp.n_subspaces)]
foreach(sp, lambda i, subspace: v_cl[subspace].add(i))
cl = set(map(frozenset, v_cl))
in_state = np.zeros((sp.dim,), dtype=float)
for op in all_ops:
for i_sp in cl:
f_sp = set()
for i in i_sp:
in_state[i] = 1.0
out_state = op * in_state
for f, a in enumerate(out_state):
if abs(a) > 1e-10:
f_sp.add(f)
in_state[i] = 0.0
# op maps i_sp onto zero
if len(f_sp) == 0:
continue
# Check if op maps i_sp to only one subspace
self.assertEqual(
sum(int(f_sp.issubset(f_sp_ref)) for f_sp_ref in cl),
1
)
def test_left_right_basis_state_indices(self):
indices = [("dn", 0), ("dn", 1), ("up", 0), ("up", 1)]
hs = HilbertSpace([make_space_fermion(*ind) for ind in indices])
# Basis of the N=1 sector
N1_basis_state_indices = [1, 2, 4, 8]
# Basis of the N=2 sector
N2_basis_state_indices = [3, 5, 6, 9, 10, 12]
# Basis of the N=3 sector
N3_basis_state_indices = [7, 11, 13, 14]
for ind1, ind2 in product(indices, indices):
op1 = LOperatorR(c(*ind1) * c(*ind2), hs)
ref1 = self.c_mat(hs, *ind1) @ self.c_mat(hs, *ind2)
ref1 = ref1[N1_basis_state_indices, :][:, N3_basis_state_indices]
assert_equal(
make_matrix(op1, N1_basis_state_indices,
N3_basis_state_indices), ref1)
op2 = LOperatorC(1j * c(*ind1) * c(*ind2), hs)
ref2 = 1j * self.c_mat(hs, *ind1) @ self.c_mat(hs, *ind2)
ref2 = ref2[N1_basis_state_indices, :][:, N3_basis_state_indices]
assert_equal(
make_matrix(op2, N1_basis_state_indices,
N3_basis_state_indices), ref2)
for ind in indices:
op1 = LOperatorR(c(*ind), hs)
ref1 = self.c_mat(hs, *ind)
ref1 = ref1[N2_basis_state_indices, :][:, N3_basis_state_indices]
assert_equal(
make_matrix(op1, N2_basis_state_indices,
N3_basis_state_indices), ref1)
op2 = LOperatorC(1j * c(*ind), hs)
ref2 = 1j * self.c_mat(hs, *ind)
ref2 = ref2[N2_basis_state_indices, :][:, N3_basis_state_indices]
assert_equal(
make_matrix(op2, N2_basis_state_indices,
N3_basis_state_indices), ref2)
def test_O_vac(self):
P = c_dag("dn", 1) * c_dag("dn", 2) + \
c_dag("dn", 1) * c_dag("up", 1) + \
c_dag("dn", 2) * c_dag("up", 1) + \
c_dag("dn", 1) * c_dag("up", 2) + \
c_dag("dn", 2) * c_dag("up", 2) + \
c_dag("up", 1) * c_dag("up", 2)
mapper = BasisMapper(LOperatorR(P, self.hs), self.hs)
self.assertEqual(len(mapper), 6)
self.assert_equal_dicts_up_to_value_permutation(mapper.map,
self.mapping)
mapper = BasisMapper(LOperatorC(1j * P, self.hs), self.hs)
self.assertEqual(len(mapper), 6)
self.assert_equal_dicts_up_to_value_permutation(mapper.map,
self.mapping)
def test_strided_arrays(self):
expr = 6 * c_dag("dn") - 6 * c("up")
hs = HilbertSpace(expr)
lop = LOperatorR(expr, hs)
src_real = 999 * np.ones((10, ), dtype=float)
src_real[3:10:2] = 1
src_complex = np.array(src_real, dtype=complex)
assert_equal(lop * src_real[3:10:2],
np.array([-6, 12, 0, 6], dtype=float))
assert_equal(lop * src_complex[3:10:2],
np.array([-6, 12, 0, 6], dtype=complex))
dst_real = 777 * np.ones((10, ), dtype=float)
dst_complex = np.array(dst_real, dtype=complex)
ref_real = np.array([777, 777, -6, 777, 12, 777, 0, 777, 6, 777],
dtype=float)
ref_complex = np.array(ref_real, dtype=complex)
lop(src_real[3:10:2], dst_real[2:9:2])
assert_equal(dst_real, ref_real)
lop(src_real[3:10:2], dst_complex[2:9:2])
assert_equal(dst_complex, ref_complex)
lop(src_complex[3:10:2], dst_complex[2:9:2])
assert_equal(dst_complex, ref_complex)
expr = 6j * c_dag("dn") - 6j * c("up")
hs = HilbertSpace(expr)
lop = LOperatorC(expr, hs)
ref_complex = np.array([777, 777, -6j, 777, 12j, 777, 0, 777, 6j, 777],
dtype=complex)
lop(src_real[3:10:2], dst_complex[2:9:2])
assert_equal(dst_complex, ref_complex)
lop(src_complex[3:10:2], dst_complex[2:9:2])
assert_equal(dst_complex, ref_complex)
def setUpClass(cls):
# Parameters of the 3 orbital Hubbard-Kanamori atom
cls.n_orbs = 3
cls.mu = 0.7
cls.U = 3.0
cls.J = 0.3
indices_up = [("up", o) for o in range(cls.n_orbs)]
indices_dn = [("dn", o) for o in range(cls.n_orbs)]
# Hamiltonian
cls.H = dispersion(-cls.mu * np.ones(cls.n_orbs), indices=indices_up)
cls.H += dispersion(-cls.mu * np.ones(cls.n_orbs), indices=indices_dn)
cls.H += kanamori_int(cls.n_orbs,
cls.U,
cls.J,
indices_up=indices_up,
indices_dn=indices_dn)
# Hilbert space
cls.hs = HilbertSpace(cls.H)
# Linear operator form of the Hamiltonian
cls.Hop = LOperatorR(cls.H, cls.hs)
H = jaynes_cummings(eps, omega, g)
# Construct state space of our problem as a direct product of two
# two-dimensional Hilbert spaces (qubits) and one truncated bosonic Hilbert
# space.
# make_space_boson(4) returns the truncated bosonic space with allowed
# occupation numbers N = 0, 1, ..., (2^4-1).
hs = HilbertSpace([
make_space_spin(1 / 2, 0), # Qubit 1: spin-1/2, index 0
make_space_spin(1 / 2, 1), # Qubit 2: spin-1/2, index 1
make_space_boson(4, 0) # Oscillator, index 0
])
# Construct a linear operator corresponding to 'H' and acting in the Hilbert
# space 'hs'.
H_op = LOperatorR(H, hs)
#
# Prepare a matrix representation of 'H_op'
#
# Method I (manual).
H_mat1 = np.zeros((hs.dim, hs.dim))
for i in range(hs.dim):
# A column vector psi = {0, 0, ..., 1, ..., 0}
psi = np.zeros(hs.dim)
psi[i] = 1.0
# Act with H_op on psi and store the result the i-th column of H_mat
H_mat1[:, i] = H_op * psi
# Method II (automatic and faster).