def cost_fn(params, generators):
# test single parameter inputs
if np.ndim(params) == 1:
params = [params[0], params[0]]
Z_1, Y_2 = circuit_1(params, generators=generators)
X_1 = circuit_2(params, generators=generators)
return 0.5 * Y_2 + 0.8 * Z_1 - 0.2 * X_1
def gradient_product(dy):
"""Returns the vector-Jacobian product with given
parameter values p and output gradient dy"""
if all(np.ndim(p) == 0 for p in params):
# only flatten dy if all parameters are single values
dy = dy.flatten()
@autograd.extend.primitive
def jacobian(p):
"""Returns the Jacobian for parameters p"""
return _evaluate_grad_matrix(p, "jacobian")
def vhp(ans, p):
def hessian_product(ddy):
"""Returns the vector-Hessian product with given
parameter values p, output gradient dy, and output
second-order gradient ddy"""
hessian = _evaluate_grad_matrix(p, "hessian")
if dy.size > 1:
vhp = dy @ ddy @ hessian @ dy.T
else:
vhp = ddy @ hessian
vhp = vhp.flatten()
return vhp
return hessian_product
# register vhp as the backward method of the jacobian function
autograd.extend.defvjp(jacobian, vhp, argnums=[0])
vjp = dy @ jacobian(params)
return vjp
def gradient_product(g):
# In autograd, the forward pass is always performed prior to the backwards
# pass, so we do not need to re-unwrap the parameters.
self.set_parameters(self._all_params_unwrapped,
trainable_only=False)
jac = self.jacobian(device, params=params, **self.jacobian_options)
self.set_parameters(self._all_parameter_values,
trainable_only=False)
# only flatten g if all parameters are single values
if all(np.ndim(p) == 0 for p in params):
vjp = g.flatten() @ jac
else:
vjp = g @ jac
return vjp
def rotosolve_step(f, x):
"""Helper function to test the Rotosolve and Rotoselect optimizers"""
# make sure that x is an array
if np.ndim(x) == 0:
x = np.array([x])
# helper function for x[d] = theta
def insert(xf, d, theta):
xf[d] = theta
return xf
for d, _ in enumerate(x):
H_0 = float(f(insert(x, d, 0)))
H_p = float(f(insert(x, d, np.pi / 2)))
H_m = float(f(insert(x, d, -np.pi / 2)))
a = onp.arctan2(2 * H_0 - H_p - H_m, H_p - H_m)
x[d] = -np.pi / 2 - a
if x[d] <= -np.pi:
x[d] += 2 * np.pi
return x