def fel_measmax(x, fun, identifier, fun_evals):
fun_none = []
vqe = vqe_nelder_mead(fatol=1e-3)
ansatz_name = identifier[1]
size = identifier[0]
for fun_evals_ in fun_evals:
fun_evals_ = int(fun_evals_)
if len(fun) < fun_evals_:
print(len(fun))
raise ValueError(f'fun_evals={fun_evals_} is to big')
fun_ = fun[:fun_evals_]
idx = np.argmin(fun_)
x_min = x[idx]
mat_idx = identifier[4]
h = hamiltonians_of_size(size)[0][mat_idx]
H, qc, ansatz_, _ = create_and_convert(ansatz_name, h)
fun_none_ = vqe.expectation(ansatz_(x_min), H, samples=None, qc=qc)[0]
fun_none.append(fun_none_)
return np.asarray(fun_none)
def input_3(size, hamiltonian_idx, V):
if V == np.inf:
e = 0.
V = 1.
else:
e = 1
h, eig = hamiltonians_of_size(size, V, e)
return h[hamiltonian_idx], eig[hamiltonian_idx]
def _num_terms(mat2op, size_min=2, size_max=101, m=25):
num_sizes = size_max - size_min + 1
nbr_terms = np.zeros(num_sizes)
matrix_size = np.zeros(num_sizes)
idx = 0
for i in range(num_sizes):
size = size_min + i
V = np.random.randn(m)
temp = 0
count = 0
for v in V:
hs = hamiltonians_of_size(size, v)[0]
for h in hs:
count += 1
temp += len(mat2op(h))
matrix_size[idx] = size
nbr_terms[idx] = temp / count
idx += 1
print(size)
return matrix_size, nbr_terms
def input_3(ansatz_name, size, hamiltonian_idx):
h, eig = hamiltonians_of_size(size)
return h[hamiltonian_idx], eig[hamiltonian_idx]
def input_5(size, ansatz_name, minimizer, repeats, hamiltonian_idx):
h, eig = hamiltonians_of_size(size)
return h[hamiltonian_idx], eig[hamiltonian_idx]
