def __init__(self, maxiter, shots):
self.qx = qxelarator.QX()
self.minimizer = minimize
self.minimizer_kwargs = {'method':'Nelder-Mead', 'options':{'maxiter':maxiter,
'ftol':1.0e-6, 'xtol':1.0e-6, 'disp':True, 'return_all':True}}
self.p_name = "test_output/qaoa_run.qasm"
self.shots = shots
self.expt = 0
def test_02():
p_name = "test_output/multi_run_02.qasm" # Works with/without kernel encapsulation
qx = qxelarator.QX()
for p_loop in range(0, 6):
qx.set(p_name)
# qx.execute(1) # Doesn't work with get_state()
qx.execute()
isv = qx.get_state()
print(isv)
def __init__(self, maxiter):
self.qx = qxelarator.QX()
self.minimizer = minimize
self.minimizer_kwargs = {'method':'Nelder-Mead', 'options':{'maxiter':maxiter,
'ftol':1.0e-6, 'xtol':1.0e-6, 'disp':True, 'return_all':True}}
# self.minimizer_kwargs = {'method':'Powell', 'options':{'maxiter':maxiter,
# 'ftol':1.0e-6, 'xtol':1.0e-6, 'disp':True}}
self.p_name = my_file = os.path.join(THIS_FOLDER, "test_output/qaoa_run.qasm")
self.expt = 0
self.probs = []
self.optstep = 0
def t_trial():
qx = qxelarator.QX()
qx.set('test_output/tomo.qasm')
p = np.zeros(2**NUM_QUBIT)
c = np.zeros(NUM_QUBIT, dtype=bool)
for i in range(NUM_TRIAL):
qx.execute()
for i in range(NUM_QUBIT):
c[i] = qx.get_measurement_outcome(i)
idx = sum(v << i for i, v in enumerate(c[::-1]))
p[idx] += 1 / NUM_TRIAL
return p
def test_03():
p_name = "test_output/multi_run_02.qasm" # Works with/without kernel encapsulation
n_qubits = 3
ptrn = re.compile('\(([+-]\d+.*\d*),([+-]\d+.*\d*)\)\s[|]([0-1]*)>')
qx = qxelarator.QX()
isv_p = []
for p_loop in range(0, 2):
isv_p = np.zeros(2**n_qubits)
qx.set(p_name)
# qx.execute(1) # Doesn't work with get_state()
qx.execute()
isv = re.findall(ptrn, qx.get_state())
for basis in iter(isv):
isv_p[int(basis[2], 2)] = float(basis[0])**2 + float(
basis[1]
)**2 # Probability is square of modulus of complex amplitude
print(isv_p)
def test_01():
p_name = "test_output/multi_run.qasm"
n_qubits = 3
shots = 400
qx = qxelarator.QX()
for p_loop in range(0, 40):
# qx = qxelarator.QX() # Inside loop cause problems
qx.set(p_name)
p = np.zeros(2**n_qubits)
c = np.zeros(n_qubits, dtype=bool)
for i in range(shots):
print(p_loop, i)
# qx.execute(1)
qx.execute() # Both works for p_loop 40 x shots 400
for i in range(n_qubits):
c[i] = qx.get_measurement_outcome(i)
idx = sum(v << i for i, v in enumerate(c[::-1]))
p[idx] += 1 / shots
print(p)