def testExecuteVQE(self):
src = """__qpu__ kernel() {
0.7137758743754461
-1.252477303982147 0 1 0 0
0.337246551663004 0 1 1 1 1 0 0 0
0.0906437679061661 0 1 1 1 3 0 2 0
0.0906437679061661 0 1 2 1 0 0 2 0
0.3317360224302783 0 1 2 1 2 0 0 0
0.0906437679061661 0 1 3 1 1 0 2 0
0.3317360224302783 0 1 3 1 3 0 0 0
0.337246551663004 1 1 0 1 0 0 1 0
0.0906437679061661 1 1 0 1 2 0 3 0
-1.252477303982147 1 1 1 0
0.0906437679061661 1 1 2 1 0 0 3 0
0.3317360224302783 1 1 2 1 2 0 1 0
0.0906437679061661 1 1 3 1 1 0 3 0
0.3317360224302783 1 1 3 1 3 0 1 0
0.3317360224302783 2 1 0 1 0 0 2 0
0.0906437679061661 2 1 0 1 2 0 0 0
0.3317360224302783 2 1 1 1 1 0 2 0
0.0906437679061661 2 1 1 1 3 0 0 0
-0.4759344611440753 2 1 2 0
0.0906437679061661 2 1 3 1 1 0 0 0
0.3486989747346679 2 1 3 1 3 0 2 0
0.3317360224302783 3 1 0 1 0 0 3 0
0.0906437679061661 3 1 0 1 2 0 1 0
0.3317360224302783 3 1 1 1 1 0 3 0
0.0906437679061661 3 1 1 1 3 0 1 0
0.0906437679061661 3 1 2 1 0 0 1 0
0.3486989747346679 3 1 2 1 2 0 3 0
-0.4759344611440753 3 1 3 0
}"""
op = vqe.compile(src)
self.assertAlmostEqual(vqe.execute(op, **{'task':'vqe', 'n-electrons':2}).energy, -1.13727, places=5)
def testExecuteDiagonalize(self):
src = """__qpu__ kernel() {
0.7137758743754461
-1.252477303982147 0 1 0 0
0.337246551663004 0 1 1 1 1 0 0 0
0.0906437679061661 0 1 1 1 3 0 2 0
0.0906437679061661 0 1 2 1 0 0 2 0
0.3317360224302783 0 1 2 1 2 0 0 0
0.0906437679061661 0 1 3 1 1 0 2 0
0.3317360224302783 0 1 3 1 3 0 0 0
0.337246551663004 1 1 0 1 0 0 1 0
0.0906437679061661 1 1 0 1 2 0 3 0
-1.252477303982147 1 1 1 0
0.0906437679061661 1 1 2 1 0 0 3 0
0.3317360224302783 1 1 2 1 2 0 1 0
0.0906437679061661 1 1 3 1 1 0 3 0
0.3317360224302783 1 1 3 1 3 0 1 0
0.3317360224302783 2 1 0 1 0 0 2 0
0.0906437679061661 2 1 0 1 2 0 0 0
0.3317360224302783 2 1 1 1 1 0 2 0
0.0906437679061661 2 1 1 1 3 0 0 0
-0.4759344611440753 2 1 2 0
0.0906437679061661 2 1 3 1 1 0 0 0
0.3486989747346679 2 1 3 1 3 0 2 0
0.3317360224302783 3 1 0 1 0 0 3 0
0.0906437679061661 3 1 0 1 2 0 1 0
0.3317360224302783 3 1 1 1 1 0 3 0
0.0906437679061661 3 1 1 1 3 0 1 0
0.0906437679061661 3 1 2 1 0 0 1 0
0.3486989747346679 3 1 2 1 2 0 3 0
-0.4759344611440753 3 1 3 0
}"""
op = vqe.compile(src)
self.assertEqual(vqe.execute(op).energy, -1.137270422069755)
def f(params):
# note 'no-mpi' at the compute energy, hamiltonian term
# level, but we are using mpi at the higher PSO swarm level
return vqe.execute(
op, **{
'mpi-provider': 'no-mpi',
'task': 'compute-energy',
'vqe-params': str(params[0]) + ',' + str(params[1])
}).energy
if k.nInstructions() > 0:
evaledAnsatz = vqe.AnsatzEvaluator.evaluate(
ansatz3x3, 2, np.asarray(params))
k.insertInstruction(0, evaledAnsatz)
qpu.execute(qubits, k)
exp = qubits.getExpectationValueZ()
qubits.resetBuffer()
val = coeff * exp
else:
val = coeff
energy += val
return energy.real
print('XACC Diagonalize: ',
vqe.execute(hamiltonian3x3,
**{}).energy) #'task':'vqe', 'ansatz':ansatz3x3}).energy)
print(
'XACC Nelder-Mead: ',
vqe.execute(hamiltonian3x3, **{
'task': 'vqe',
'vqe-params': '.7,.2',
'ansatz': ansatz3x3
}).energy)
print('XACC SciPy Minimze:\n', minimize(energy, [0, 0]))
from bayes_opt import BayesianOptimization
def neg_energy(x, y):
return -1 * energy([x, y])
ham = PauliOperator(5.906709445) + PauliOperator({
0: 'X',
1: 'X'
}, -2.1433) + PauliOperator({
0: 'Y',
1: 'Y'
}, -2.1433) + PauliOperator({0: 'Z'}, .21829) + PauliOperator({1: 'Z'}, -6.125)
print('\nH_{2x2} = ', ham)
src = """__qpu__ f(AcceleratorBuffer ab, double t0) {
X 0
RY(t0) 1
CNOT 1 0
}"""
p = xacc.Program(xacc.getAccelerator('tnqvm'), src)
print('Program built python')
k = p.getKernel('f')
print('Got Kernel')
ansatz = k.getIRFunction()
print('Ansztz = ', ansatz.name())
print('Ansatz = \n', ansatz.toString('q'))
angle_range = np.linspace(-np.pi, np.pi, 50)
vqeResult = vqe.execute(ham, **{'task': 'vqe', 'ansatz': ansatz})
print('(Optimal Angle, Energy) = (', vqeResult.angles, ',', vqeResult.energy,
')')
print('Number of QPU Calls = ', vqeResult.nQpuCalls)
print('Number of VQE Iterations = ', vqeResult.vqeIterations)