def run1():
t = 3.0
M = 100
delta = t / M
h = 3.0
n_qubits = 6
coefs = ([(1.0, [("Z", i), ("Z", (i + 1) % n_qubits)])
for i in range(n_qubits)] + [(h, [("X", i)])
for i in range(n_qubits)])
hamiltonian = Hamiltonian(n_qubits, coefs)
circuit = hamiltonian.time_evolution_circuit(delta)
# target observable
z_obs = qulacs.Observable(n_qubits)
for i in range(n_qubits):
z_obs.add_operator(qulacs.PauliOperator("Z " + str(i), 1.0 / n_qubits))
# target observable
z_obs = qulacs.Observable(n_qubits)
for i in range(n_qubits):
z_obs.add_operator(qulacs.PauliOperator("Z " + str(i), 1.0 / n_qubits))
# initial state
state = qulacs.QuantumState(n_qubits)
state.set_zero_state()
# time evolution
t = [i * delta for i in range(M + 1)]
y = list()
y.append(z_obs.get_expectation_value(state))
for i in range(M):
circuit.update_quantum_state(state)
y.append(z_obs.get_expectation_value(state))
plt.plot(t, y)
plt.show()
def initialize_hamiltonian(self, hamiltonians):
result = []
for H in hamiltonians:
if self.use_mapping:
# initialize only the active parts of the Hamiltonian and pre-evaluate the passive ones
# passive parts are the components of each individual pauli string which act on qubits where the circuit does not act on
# if the circuit does not act on those qubits the passive parts are always evaluating to 1 (if the pauli operator is Z) or 0 (otherwise)
# since those qubits are always in state |0>
non_zero_strings = []
unit_strings = []
for ps in H.paulistrings:
string = ""
for k, v in ps.items():
if k in self.U.qubit_map:
string += v.upper() + " " + str(
self.U.qubit_map[k]) + " "
elif v.upper() != "Z":
string = "ZERO"
break
string = string.strip()
if string != "ZERO":
non_zero_strings.append((ps.coeff, string))
elif string == "":
unit_strings.append((ps.coeff, string))
# accumulate unit strings
if len(unit_strings) > 0:
coeffs = [x[0] for x in unit_strings]
result.append(sum(coeffs))
if len(non_zero_strings) > 0:
qulacs_H = qulacs.Observable(self.n_qubits)
for coeff, string in non_zero_strings:
qulacs_H.add_operator(coeff, string)
result.append(qulacs_H)
else:
if self.U.n_qubits < H.n_qubits:
raise TequilaQulacsException(
"Hamiltonian has more qubits as the Unitary. Mapped expectationvalues are switched off"
)
qulacs_H = qulacs.Observable(self.n_qubits)
for ps in H.paulistrings:
string = ""
for k, v in ps.items():
string += v.upper() + " " + str(k)
qulacs_H.add_operator(ps.coeff, string)
result.append(qulacs_H)
return result
def initialize_hamiltonian(self, hamiltonians):
"""
Convert reduced hamiltonians to native Qulacs types for efficient expectation value evaluation.
Parameters
----------
hamiltonians:
an interable set of hamiltonian objects.
Returns
-------
list:
initialized hamiltonian objects.
"""
# map the reduced operators to the potentially smaller qubit system
qubit_map = {}
for i, q in enumerate(self.U.abstract_circuit.qubits):
qubit_map[q] = i
result = []
for H in hamiltonians:
qulacs_H = qulacs.Observable(self.n_qubits)
for ps in H.paulistrings:
string = ""
for k, v in ps.items():
string += v.upper() + " " + str(qubit_map[k])
qulacs_H.add_operator(ps.coeff, string)
result.append(qulacs_H)
return result
def initialize_hamiltonian(self, hamiltonians):
"""
Convert hamiltonian to native Qulacs types for efficient expectation value evaluation.
Parameters
----------
hamiltonians:
an interable set of hamiltonian objects.
Returns
-------
list:
initialized hamiltonian objects.
"""
result = []
for H in hamiltonians:
qulacs_H = qulacs.Observable(self.n_qubits)
for ps in H.paulistrings:
string = ""
for k, v in ps.items():
string += v.upper() + " " + str(k)
qulacs_H.add_operator(ps.coeff, string)
result.append(qulacs_H)
return result
def run2():
# Hamiltonian
n_qubits = 6
h = 3.0
coefs = ([(1.0, [("Z", i), ("Z", (i + 1) % n_qubits)])
for i in range(n_qubits)] + [(h, [("X", i)])
for i in range(n_qubits)])
hamiltonian = TIDHamiltonian(n_qubits, coefs)
# target observable
z_obs = qulacs.Observable(n_qubits)
for i in range(n_qubits):
z_obs.add_operator(qulacs.PauliOperator("Z " + str(i), 1.0 / n_qubits))
# dynamics
t = 3.0
nt = 100
dy = QuantumDynamics(hamiltonian, t, nt, z_obs)
(t, y) = dy.run()
# plot
plt.plot(t, y)
plt.show()