def test_symbolic_term_matrix(backend):
"""Test matrix calculation of ``SymbolicTerm``."""
from qibo.symbols import X, Y, Z
expression = X(0) * Y(1) * Z(2) * X(1)
term = terms.SymbolicTerm(2, expression)
assert term.target_qubits == (0, 1, 2)
target_matrix = np.kron(matrices.X, matrices.Y @ matrices.X)
target_matrix = 2 * np.kron(target_matrix, matrices.Z)
K.assert_allclose(term.matrix, target_matrix)
def test_symbolic_term_mul(backend):
"""Test multiplying scalar to ``SymbolicTerm``."""
from qibo.symbols import X, Y, Z
expression = Y(2) * Z(3) * X(2) * X(3)
term = terms.SymbolicTerm(1, expression)
assert term.target_qubits == (2, 3)
target_matrix = np.kron(matrices.Y @ matrices.X, matrices.Z @ matrices.X)
K.assert_allclose(term.matrix, target_matrix)
K.assert_allclose((2 * term).matrix, 2 * target_matrix)
K.assert_allclose((term * 3).matrix, 3 * target_matrix)
def test_symbolic_term_with_power_creation():
"""Test creating ``SymbolicTerm`` from sympy expression that contains powers."""
from qibo.symbols import X, Z
expression = X(0)**4 * Z(1)**2 * X(2)
term = terms.SymbolicTerm(2, expression)
assert term.target_qubits == (0, 1, 2)
assert len(term.matrix_map) == 3
assert term.coefficient == 2
K.assert_allclose(term.matrix_map.get(0), 4 * [matrices.X])
K.assert_allclose(term.matrix_map.get(1), 2 * [matrices.Z])
K.assert_allclose(term.matrix_map.get(2), [matrices.X])
def test_symbolicxxz_hamiltonian_to_dense(backend, nqubits, calcterms):
from qibo.symbols import X, Y, Z
sham = sum(X(i) * X(i + 1) for i in range(nqubits - 1))
sham += sum(Y(i) * Y(i + 1) for i in range(nqubits - 1))
sham += 0.5 * sum(Z(i) * Z(i + 1) for i in range(nqubits - 1))
sham += X(0) * X(nqubits - 1) + Y(0) * Y(nqubits -
1) + 0.5 * Z(0) * Z(nqubits - 1)
final_ham = hamiltonians.SymbolicHamiltonian(sham)
target_ham = hamiltonians.XXZ(nqubits)
if calcterms:
_ = final_ham.terms
K.assert_allclose(final_ham.matrix, target_ham.matrix, atol=1e-15)
def test_term_group_to_term(backend):
"""Test ``GroupTerm.term`` property."""
from qibo.symbols import X, Z
matrix = np.random.random((8, 8))
term1 = terms.HamiltonianTerm(matrix, 0, 1, 3)
term2 = terms.SymbolicTerm(1, X(0) * Z(3))
term3 = terms.SymbolicTerm(2, X(1))
group = terms.TermGroup(term1)
group.append(term2)
group.append(term3)
matrix2 = np.kron(np.kron(matrices.X, np.eye(2)), matrices.Z)
matrix3 = np.kron(np.kron(np.eye(2), matrices.X), np.eye(2))
target_matrix = matrix + matrix2 + 2 * matrix3
K.assert_allclose(group.term.matrix, target_matrix)
def tsp_mixer(num_cities):
two_to_one = calculate_two_to_one(num_cities)
splus = lambda u, i: X(int(two_to_one[u, i])) + 1j * Y(int(two_to_one[u, i]))
sminus = lambda u, i: X(int(two_to_one[u, i])) - 1j * Y(int(two_to_one[u, i]))
form = 0
for i in range(num_cities):
for u in range(num_cities):
for v in range(num_cities):
if u != v:
form += splus(u, i) * splus(v, (i + 1) % num_cities) * sminus(u, (
i + 1) % num_cities) * sminus(v, i) + sminus(u, i) * sminus(v, (
i + 1) % num_cities) * splus(u, (i + 1) % num_cities) * splus(
v, i)
ham = SymbolicHamiltonian(form)
return ham
def test_symbolic_hamiltonian_abstract_symbol_ev(backend, density_matrix,
calcterms):
from qibo.symbols import X, Symbol
matrix = np.random.random((2, 2))
form = X(0) * Symbol(1, matrix) + Symbol(0, matrix) * X(1)
local_ham = hamiltonians.SymbolicHamiltonian(form)
if calcterms:
_ = local_ham.terms
if density_matrix:
state = K.cast(random_complex((4, 4)))
else:
state = K.cast(random_complex((4, )))
local_ev = local_ham.expectation(state)
target_ev = local_ham.dense.expectation(state)
K.assert_allclose(local_ev, target_ev)
def test_hamiltonian_with_identity_symbol(calcterms):
"""Check creating Hamiltonian from expression which contains the identity symbol."""
from qibo.symbols import I, X, Y, Z
symham = X(0) * I(1) * Z(2) + 0.5 * Y(0) * Z(1) * I(3) + Z(0) * I(1) * X(2)
ham = hamiltonians.SymbolicHamiltonian(symham)
if calcterms:
_ = ham.terms
final_matrix = ham.matrix
target_matrix = np.kron(np.kron(matrices.X, matrices.I),
np.kron(matrices.Z, matrices.I))
target_matrix += 0.5 * np.kron(np.kron(matrices.Y, matrices.Z),
np.kron(matrices.I, matrices.I))
target_matrix += np.kron(np.kron(matrices.Z, matrices.I),
np.kron(matrices.X, matrices.I))
K.assert_allclose(final_matrix, target_matrix)
def test_tfim_hamiltonian_from_symbols(nqubits, hamtype, calcterms):
"""Check creating TFIM Hamiltonian using sympy."""
if hamtype == "symbolic":
from qibo.symbols import X, Z
h = 0.5
symham = sum(Z(i) * Z(i + 1) for i in range(nqubits - 1))
symham += Z(0) * Z(nqubits - 1)
symham += h * sum(X(i) for i in range(nqubits))
ham = hamiltonians.SymbolicHamiltonian(-symham)
else:
h = 0.5
z_symbols = sympy.symbols(" ".join((f"Z{i}" for i in range(nqubits))))
x_symbols = sympy.symbols(" ".join((f"X{i}" for i in range(nqubits))))
symham = sum(z_symbols[i] * z_symbols[i + 1] for i in range(nqubits - 1))
symham += z_symbols[0] * z_symbols[-1]
symham += h * sum(x_symbols)
symmap = {z: (i, matrices.Z) for i, z in enumerate(z_symbols)}
symmap.update({x: (i, matrices.X) for i, x in enumerate(x_symbols)})
ham = hamiltonians.Hamiltonian.from_symbolic(-symham, symmap)
if calcterms:
_ = ham.terms
final_matrix = ham.matrix
target_matrix = hamiltonians.TFIM(nqubits, h=h).matrix
K.assert_allclose(final_matrix, target_matrix)
def symbolic_tfim(nqubits, h=1.0):
"""Constructs symbolic Hamiltonian for TFIM."""
from qibo.symbols import Z, X
sham = -sum(Z(i) * Z(i + 1) for i in range(nqubits - 1))
sham -= Z(0) * Z(nqubits - 1)
sham -= h * sum(X(i) for i in range(nqubits))
return sham
def test_symbolic_term_merge(backend):
"""Test merging ``SymbolicTerm`` to ``HamiltonianTerm``."""
from qibo.symbols import X, Z
matrix = np.random.random((4, 4))
term1 = terms.HamiltonianTerm(matrix, 0, 1)
term2 = terms.SymbolicTerm(1, X(0) * Z(1))
term = term1.merge(term2)
target_matrix = matrix + np.kron(matrices.X, matrices.Z)
K.assert_allclose(term.matrix, target_matrix)
def test_three_qubit_term_hamiltonian_from_symbols(hamtype, calcterms):
"""Check creating Hamiltonian with three-qubit interaction using sympy."""
if hamtype == "symbolic":
from qibo.symbols import X, Y, Z
symham = X(0) * Y(1) * Z(2) + 0.5 * Y(0) * Z(1) * X(3) + Z(0) * X(2)
symham += Y(2) + 1.5 * Z(1) - 2 - 3 * X(1) * Y(3)
ham = hamiltonians.SymbolicHamiltonian(symham)
else:
x_symbols = sympy.symbols(" ".join((f"X{i}" for i in range(4))))
y_symbols = sympy.symbols(" ".join((f"Y{i}" for i in range(4))))
z_symbols = sympy.symbols(" ".join((f"Z{i}" for i in range(4))))
symmap = {x: (i, matrices.X) for i, x in enumerate(x_symbols)}
symmap.update({x: (i, matrices.Y) for i, x in enumerate(y_symbols)})
symmap.update({x: (i, matrices.Z) for i, x in enumerate(z_symbols)})
symham = x_symbols[0] * y_symbols[1] * z_symbols[2]
symham += 0.5 * y_symbols[0] * z_symbols[1] * x_symbols[3]
symham += z_symbols[0] * x_symbols[2]
symham += -3 * x_symbols[1] * y_symbols[3]
symham += y_symbols[2]
symham += 1.5 * z_symbols[1]
symham -= 2
ham = hamiltonians.Hamiltonian.from_symbolic(symham, symmap)
if calcterms:
_ = ham.terms
final_matrix = ham.matrix
target_matrix = np.kron(np.kron(matrices.X, matrices.Y),
np.kron(matrices.Z, matrices.I))
target_matrix += 0.5 * np.kron(np.kron(matrices.Y, matrices.Z),
np.kron(matrices.I, matrices.X))
target_matrix += np.kron(np.kron(matrices.Z, matrices.I),
np.kron(matrices.X, matrices.I))
target_matrix += -3 * np.kron(np.kron(matrices.I, matrices.X),
np.kron(matrices.I, matrices.Y))
target_matrix += np.kron(np.kron(matrices.I, matrices.I),
np.kron(matrices.Y, matrices.I))
target_matrix += 1.5 * np.kron(np.kron(matrices.I, matrices.Z),
np.kron(matrices.I, matrices.I))
target_matrix -= 2 * np.eye(2**4, dtype=target_matrix.dtype)
K.assert_allclose(final_matrix, target_matrix)
def test_symbolic_term_creation(use_symbols):
"""Test creating ``SymbolicTerm`` from sympy expression."""
if use_symbols:
from qibo.symbols import X, Y
expression = X(0) * Y(1) * X(1)
symbol_map = {}
else:
import sympy
x0, x1, y1 = sympy.symbols("X0 X1 Y1", commutative=False)
expression = x0 * y1 * x1
symbol_map = {
x0: (0, matrices.X),
x1: (1, matrices.X),
y1: (1, matrices.Y)
}
term = terms.SymbolicTerm(2, expression, symbol_map)
assert term.target_qubits == (0, 1)
assert len(term.matrix_map) == 2
K.assert_allclose(term.matrix_map.get(0)[0], matrices.X)
K.assert_allclose(term.matrix_map.get(1)[0], matrices.Y)
K.assert_allclose(term.matrix_map.get(1)[1], matrices.X)
def test_from_symbolic_with_complex_numbers(hamtype, calcterms):
"""Check ``from_symbolic`` when the expression contains imaginary unit."""
if hamtype == "symbolic":
from qibo.symbols import X, Y
symham = (1 + 2j) * X(0) * X(1) + 2 * Y(0) * Y(1) - 3j * X(0) * Y(1) + 1j * Y(0) * X(1)
ham = hamiltonians.SymbolicHamiltonian(symham)
else:
x = sympy.symbols(" ".join((f"X{i}" for i in range(2))))
y = sympy.symbols(" ".join((f"Y{i}" for i in range(2))))
symham = (1 + 2j) * x[0] * x[1] + 2 * y[0] * y[1] - 3j * x[0] * y[1] + 1j * y[0] * x[1]
symmap = {s: (i, matrices.X) for i, s in enumerate(x)}
symmap.update({s: (i, matrices.Y) for i, s in enumerate(y)})
ham = hamiltonians.Hamiltonian.from_symbolic(symham, symmap)
if calcterms:
_ = ham.terms
final_matrix = ham.matrix
target_matrix = (1 + 2j) * np.kron(matrices.X, matrices.X)
target_matrix += 2 * np.kron(matrices.Y, matrices.Y)
target_matrix -= 3j * np.kron(matrices.X, matrices.Y)
target_matrix += 1j * np.kron(matrices.Y, matrices.X)
K.assert_allclose(final_matrix, target_matrix)
def test_x_hamiltonian_from_symbols(nqubits, hamtype, calcterms):
"""Check creating sum(X) Hamiltonian using sympy."""
if hamtype == "symbolic":
from qibo.symbols import X
symham = -sum(X(i) for i in range(nqubits))
ham = hamiltonians.SymbolicHamiltonian(symham)
else:
x_symbols = sympy.symbols(" ".join((f"X{i}" for i in range(nqubits))))
symham = -sum(x_symbols)
symmap = {x: (i, matrices.X) for i, x in enumerate(x_symbols)}
ham = hamiltonians.Hamiltonian.from_symbolic(symham, symmap)
if calcterms:
_ = ham.terms
final_matrix = ham.matrix
target_matrix = hamiltonians.X(nqubits).matrix
K.assert_allclose(final_matrix, target_matrix)
def test_symbolic_term_call(backend, density_matrix):
"""Test applying ``SymbolicTerm`` to state."""
from qibo.symbols import X, Y, Z
expression = Z(0) * X(1) * Y(2)
term = terms.SymbolicTerm(2, expression)
matrixlist = [
np.kron(matrices.Z, np.eye(4)),
np.kron(np.kron(np.eye(2), matrices.X), np.eye(2)),
np.kron(np.eye(4), matrices.Y)
]
initial_state = random_density_matrix(
3) if density_matrix else random_state(3)
final_state = term(np.copy(initial_state), density_matrix=density_matrix)
target_state = 2 * np.copy(initial_state)
for matrix in matrixlist:
target_state = matrix @ target_state
K.assert_allclose(final_state, target_state)
