def test_scipy_optimizer():
def test_cost_function(x, nshots):
noise = 1 / np.sqrt(nshots) * np.random.randn()
out = (np.cos(x[0]) * np.sin(x[1]) + noise, 1 / np.sqrt(nshots))
return out
x0 = [0, -1]
res = scipy_optimizer(test_cost_function, x0, nshots=10000)
print(res)
assert np.allclose(res['fun'], -1, rtol=1.1)
assert np.allclose(res['x'], [0, -np.pi / 2], rtol=1.5, atol=0.5)
def test_qaoa_on_WFSim():
params = FourierParams.linear_ramp_from_hamiltonian(hamiltonian,
n_steps=10,
q=2)
p0 = params.raw()
sim = WavefunctionSimulator()
cost_fun = QAOACostFunctionOnWFSim(hamiltonian=hamiltonian,
params=params,
sim=sim,
return_standard_deviation=True,
noisy=False)
with local_qvm():
out = scipy_optimizer(cost_fun, p0, epsilon=1e-3)
print(out)
def test_parameter_infos():
params = ExtendedParams.linear_ramp_from_hamiltonian(hamiltonian,
n_steps=2)
print(params)
p0 = params.raw()
print(p0)
sim = WavefunctionSimulator()
cost_fun = QAOACostFunctionOnWFSim(hamiltonian=hamiltonian,
params=params,
sim=sim,
scalar_cost_function=True,
nshots=1,
noisy=False)
with local_qvm():
out = scipy_optimizer(cost_fun, p0, epsilon=1e-3)
print(out)
def test_vqe_on_WFSim():
sim = WavefunctionSimulator()
cost_fun = PrepareAndMeasureOnWFSim(
prepare_ansatz=prepare_ansatz,
make_memory_map=lambda p: {"params": p},
hamiltonian=hamiltonian,
sim=sim,
return_standard_deviation=True,
noisy=False)
with local_qvm():
out = scipy_optimizer(cost_fun, p0, epsilon=1e-3)
print(out)
wf = sim.wavefunction(prepare_ansatz, {"params": out['x']})
assert np.allclose(np.abs(wf.amplitudes**2), [0, 0, 0, 1],
rtol=1.5,
atol=0.01)
assert np.allclose(out['fun'], -1.3)
assert out['success']
def test_vqe_on_QVM_QubitPlaceholders():
qubit_mapping = {q0: 0, q1: 1}
p0 = [3.1, -1.5, 0, 0] # make it easier when sampling
qvm = get_qc("2q-qvm")
with local_qvm():
cost_fun = PrepareAndMeasureOnQVM(
prepare_ansatz=prepare_ansatz,
make_memory_map=lambda p: {"params": p},
hamiltonian=hamiltonian,
qvm=qvm,
scalar_cost_function=False,
base_numshots=50,
enable_logging=True,
qubit_mapping=qubit_mapping)
out = scipy_optimizer(cost_fun, p0, epsilon=1e-2, nshots=4)
print(out)
print(cost_fun.log)
assert np.allclose(out['fun'], -4, rtol=1.1)
assert out['success']
def test_vqe_on_WFSim_QubitPlaceholders():
qubit_mapping = get_default_qubit_mapping(prepare_ansatz)
sim = WavefunctionSimulator()
cost_fun = PrepareAndMeasureOnWFSim(
prepare_ansatz=prepare_ansatz,
make_memory_map=lambda p: {"params": p},
hamiltonian=hamiltonian,
sim=sim,
scalar_cost_function=False,
noisy=False,
qubit_mapping=qubit_mapping)
with local_qvm():
out = scipy_optimizer(cost_fun, p0, epsilon=1e-3)
print(out)
prog = address_qubits(prepare_ansatz, qubit_mapping=qubit_mapping)
wf = sim.wavefunction(prog, {"params": out['x']})
print(wf.probabilities())
assert np.allclose(np.abs(wf.amplitudes**2), [0, 0, 0, 1],
rtol=1.5,
atol=0.01)
assert np.allclose(out['fun'], -4)
assert out['success']