def test_evolution_operator(n_qubits, n_levels):
#generate random parameters
params = 0.01*np.random.rand(2, n_levels)
gammas = params[0]
betas = params[1]
#generate random graph
edges = []
while len(edges) < 1:
prob = 0.5
graph = erdos_renyi_graph(n_qubits, prob)
edges = list(graph.edges)
#generate random n-qubits state
list_gen_state = qucs.n_rand_qubits(n_qubits)
gen_state = qu.tensor(list_gen_state)
#Test if it works as expected
obs = qaoa.evolution_operator(n_qubits, edges, gammas, betas)*gen_state
exp = gen_state
for i in range(len(gammas)):
u_mix_hamilt_i = (-complex(0,betas[i])*qaoa.mix_hamilt(n_qubits)).expm()
u_prob_hamilt_i = (-complex(0,gammas[i])*qaoa.prob_hamilt(n_qubits, edges)).expm()
exp = u_mix_hamilt_i*u_prob_hamilt_i*exp
assert (np.round(np.array(exp.full()), 8) == (np.round(np.array(obs.full()), 8))).all()
#test if it evolves a state for known parameters
exp = qu.qload('final_state_simple_graph_p=1')
obs = qaoa.evolution_operator(3, [(0,1),(1,2)], [1.0], [0.4])*qaoa.initial_state(3)
def test_comp_basis_prob_dist(n_qubits):
#generate a generic n-qubits state
list_gen_state = qucs.n_rand_qubits(n_qubits)
gen_state = qu.tensor(list_gen_state)
#Test that, gor a generic qstate, sum probabilities is 1
exp = 1.0
obs = round(sum(qucs.comp_basis_prob_dist(gen_state)), 14)
assert_equal(exp, obs)
def test_mix_hamilt(n_qubits):
#generate a generic n-qubits state
list_gen_state = qucs.n_rand_qubits(n_qubits)
gen_state = qu.tensor(list_gen_state)
#test is ìf the result is the one expected
obs = qaoa.mix_hamilt(n_qubits)*gen_state
list_exp = []
for i in range(0,n_qubits):
list_exp.append(qucs.n_sigmax(n_qubits,i)*gen_state)
exp = sum(list_exp)
assert_equal(obs,exp)
def test_n_ranf_qubits(n_qubits):
#Initialazing the initial state
list_qubits = qucs.n_rand_qubits(n_qubits)
#Test if the shape of each qubit is (2,1)
for i in range(n_qubits):
exp = (2,1)
obs = list_qubits[i].shape
assert_equal(exp,obs)
#Test if each qubit is a ket
for i in range(n_qubits):
exp = 'ket'
obs = list_qubits[i].type
assert_equal(exp,obs)
def test_n_sigmaz(n_qubits):
#generate a generic n-qubits state
list_gen_state = qucs.n_rand_qubits(n_qubits)
gen_state = qu.tensor(list_gen_state)
#Test if n_sigmaz applies sigmaz on qubit in qubit_pos
qubit_pos = np.random.randint(0,n_qubits)
list_exp = []
for i in range(n_qubits):
if i == qubit_pos:
list_exp.append(qu.sigmaz()*list_gen_state[i])
else:
list_exp.append(list_gen_state[i])
exp = qu.tensor(list_exp)
obs = qucs.n_sigmaz(n_qubits,qubit_pos)*gen_state
assert_equal(exp, obs)
def test_prob_hamilt(n_qubits):
#generate a generic n-qubits state
list_gen_state = qucs.n_rand_qubits(n_qubits)
gen_state = qu.tensor(list_gen_state)
#generate a random graph of n-vertices
edges = []
while len(edges) < 1:
prob = 0.5
graph = erdos_renyi_graph(n_qubits, prob)
edges = list(graph.edges)
#test is ìf the result is the one expected
obs = qaoa.prob_hamilt(n_qubits,edges)*gen_state
list_exp = []
for j in range(0,len(edges)):
list_exp.append(0.5*(qucs.n_qeye(n_qubits)
-qucs.n_sigmaz(n_qubits,edges[j][0])*qucs.n_sigmaz(n_qubits,edges[j][1]))*gen_state)
exp = sum(list_exp)
assert_equal(obs,exp)