def test_single_step(self, qnode, param, num_freq, optimizer, optimizer_kwargs):
opt = RotosolveOptimizer()
repack_param = len(param) == 1
new_param_step = opt.step(
qnode,
*param,
num_freqs=num_freq,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
)
if repack_param:
new_param_step = (new_param_step,)
assert (np.isscalar(new_param_step) and np.isscalar(param)) or len(new_param_step) == len(
param
)
new_param_step_and_cost, old_cost = opt.step_and_cost(
qnode,
*param,
num_freqs=num_freq,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
)
if repack_param:
new_param_step_and_cost = (new_param_step_and_cost,)
assert np.allclose(
np.fromiter(_flatten(new_param_step_and_cost), dtype=float),
np.fromiter(_flatten(new_param_step), dtype=float),
)
assert np.isclose(qnode(*param), old_cost)
def test_single_step(self, qnode, param, nums_frequency, spectra,
substep_optimizer, substep_kwargs):
"""Test executing a single step of the RotosolveOptimizer on a QNode."""
param = tuple(np.array(p, requires_grad=True) for p in param)
opt = RotosolveOptimizer(substep_optimizer, substep_kwargs)
repack_param = len(param) == 1
new_param_step = opt.step(
qnode,
*param,
nums_frequency=nums_frequency,
spectra=spectra,
)
if repack_param:
new_param_step = (new_param_step, )
assert (np.isscalar(new_param_step)
and np.isscalar(param)) or len(new_param_step) == len(param)
new_param_step_and_cost, old_cost = opt.step_and_cost(
qnode,
*param,
nums_frequency=nums_frequency,
spectra=spectra,
)
if repack_param:
new_param_step_and_cost = (new_param_step_and_cost, )
assert np.allclose(
np.fromiter(_flatten(new_param_step_and_cost), dtype=float),
np.fromiter(_flatten(new_param_step), dtype=float),
)
assert np.isclose(qnode(*param), old_cost)
def test_single_step_convergence(self, fun, x_min, param, nums_freq,
exp_num_calls, substep_optimizer,
substep_kwargs):
"""Tests convergence for easy classical functions in a single Rotosolve step.
Includes testing of the parameter output shape and the old cost when using step_and_cost."""
opt = RotosolveOptimizer(substep_optimizer, substep_kwargs)
# Make only the first argument trainable
param = (np.array(param[0], requires_grad=True), ) + param[1:]
# Only one argument is marked as trainable -> All other arguments have to stay fixed
new_param_step = opt.step(
fun,
*param,
nums_frequency=nums_freq,
)
# The following accounts for the unpacking functionality for length-1 param
if len(param) == 1:
new_param_step = (new_param_step, )
assert all(
np.allclose(p, new_p)
for p, new_p in zip(param[1:], new_param_step[1:]))
# With trainable parameters, training should happen
param = tuple(np.array(p, requires_grad=True) for p in param)
new_param_step = opt.step(
fun,
*param,
nums_frequency=nums_freq,
)
# The following accounts for the unpacking functionality for length-1 param
if len(param) == 1:
new_param_step = (new_param_step, )
assert len(x_min) == len(new_param_step)
assert np.allclose(
np.fromiter(_flatten(x_min), dtype=float),
np.fromiter(_flatten(new_param_step), dtype=float),
atol=1e-5,
)
# Now with step_and_cost and trainable params
new_param_step_and_cost, old_cost = opt.step_and_cost(
fun,
*param,
nums_frequency=nums_freq,
)
# The following accounts for the unpacking functionality for length-1 param
if len(param) == 1:
new_param_step_and_cost = (new_param_step_and_cost, )
assert len(x_min) == len(new_param_step_and_cost)
assert np.allclose(
np.fromiter(_flatten(new_param_step_and_cost), dtype=float),
np.fromiter(_flatten(new_param_step), dtype=float),
atol=1e-5,
)
assert np.isclose(old_cost, fun(*param))
def successive_params(par1, par2):
"""Return a list of parameter configurations, successively walking from
par1 to par2 coordinate-wise."""
par1_flat = np.fromiter(_flatten(par1), dtype=float)
par2_flat = np.fromiter(_flatten(par2), dtype=float)
walking_param = []
for i in range(len(par1_flat) + 1):
walking_param.append(unflatten(np.append(par2_flat[:i], par1_flat[i:]), par1))
return walking_param
def test_number_of_function_calls(
self, fun, x_min, param, num_freq, optimizer, optimizer_kwargs
):
"""Tests that per parameter 2R+1 function calls are used for an update step."""
global num_calls
num_calls = 0
def _fun(*args, **kwargs):
global num_calls
num_calls += 1
return fun(*args, **kwargs)
opt = RotosolveOptimizer()
new_param = opt.step(
_fun,
*param,
num_freqs=num_freq,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
)
expected_num_calls = np.sum(
np.fromiter(_flatten(expand_num_freq(num_freq, param)), dtype=int) * 2 + 1
)
assert num_calls == expected_num_calls
def step(self, objective_fn, x, generators):
r"""Update x with one step of the optimizer.
Args:
objective_fn (function): The objective function for optimization. It must have the
signature ``objective_fn(x, generators=None)`` with a sequence of the values ``x``
and a list of the gates ``generators`` as inputs, returning a single value.
x (Union[Sequence[float], float]): Sequence containing the initial values of the
variables to be optimized over, or a single float with the initial value.
generators (list[~.Operation]): List containing the initial ``pennylane.ops.qubit``
operators to be used in the circuit and optimized over.
Returns:
array: The new variable values :math:`x^{(t+1)}` as well as the new generators.
"""
x_flat = np.fromiter(_flatten(x), dtype=float)
try:
assert len(x_flat) == len(generators)
except AssertionError:
raise ValueError(
"Number of parameters {} must be equal to the number of generators.".format(x)
)
for d, _ in enumerate(x_flat):
x_flat[d], generators[d] = self._find_optimal_generators(
objective_fn, x_flat, generators, d
)
return unflatten(x_flat, x), generators
def test_single_step_convergence(
self, fun, x_min, param, num_freq, optimizer, optimizer_kwargs
):
"""Tests convergence for easy classical functions in a single Rotosolve step.
Includes testing of the parameter output shape and the old cost when using step_and_cost."""
opt = RotosolveOptimizer()
new_param_step = opt.step(
fun,
*param,
num_freqs=num_freq,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
)
# The following accounts for the unpacking functionality for length-1 param
if len(param) == 1:
new_param_step = (new_param_step,)
assert len(x_min) == len(new_param_step)
assert np.allclose(
np.fromiter(_flatten(x_min), dtype=float),
np.fromiter(_flatten(new_param_step), dtype=float),
atol=1e-5,
)
new_param_step_and_cost, old_cost = opt.step_and_cost(
fun,
*param,
num_freqs=num_freq,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
)
# The following accounts for the unpacking functionality for length-1 param
if len(param) == 1:
new_param_step_and_cost = (new_param_step_and_cost,)
assert len(x_min) == len(new_param_step_and_cost)
assert np.allclose(
np.fromiter(_flatten(new_param_step_and_cost), dtype=float),
np.fromiter(_flatten(new_param_step), dtype=float),
atol=1e-5,
)
assert np.isclose(old_cost, fun(*param))
def test_single_step(self, fun, x_min, param, num_freq):
"""Tests convergence for easy classical functions in a single Rotosolve step
with some arguments deactivated for training.
Includes testing of the parameter output shape and the old cost when using step_and_cost."""
substep_optimizer = "brute"
substep_kwargs = None
opt = RotosolveOptimizer(substep_optimizer, substep_kwargs)
new_param_step = opt.step(
fun,
*param,
nums_frequency=num_freq,
)
# The following accounts for the unpacking functionality for length-1 param
if len(param) == 1:
new_param_step = (new_param_step, )
assert len(x_min) == len(new_param_step)
assert np.allclose(
np.fromiter(_flatten(x_min), dtype=float),
np.fromiter(_flatten(new_param_step), dtype=float),
atol=1e-5,
)
new_param_step_and_cost, old_cost = opt.step_and_cost(
fun,
*param,
nums_frequency=num_freq,
)
# The following accounts for the unpacking functionality for length-1 param
if len(param) == 1:
new_param_step_and_cost = (new_param_step_and_cost, )
assert len(x_min) == len(new_param_step_and_cost)
assert np.allclose(
np.fromiter(_flatten(new_param_step_and_cost), dtype=float),
np.fromiter(_flatten(new_param_step), dtype=float),
atol=1e-5,
)
assert np.isclose(old_cost, fun(*param))
def test_multiple_steps(fun, x_min, param, num_freq):
"""Tests that repeated steps execute as expected."""
opt = RotosolveOptimizer()
optimizer = "brute"
optimizer_kwargs = None
for _ in range(3):
param = opt.step(
fun,
*param,
num_freqs=num_freq,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
)
# The following accounts for the unpacking functionality for length-1 param
if len(x_min) == 1:
param = (param,)
assert (np.isscalar(x_min) and np.isscalar(param)) or len(x_min) == len(param)
assert np.allclose(
np.fromiter(_flatten(x_min), dtype=float),
np.fromiter(_flatten(param), dtype=float),
atol=1e-5,
)
def test_multiple_steps(fun, x_min, param, num_freq):
"""Tests that repeated steps execute as expected."""
param = tuple(np.array(p, requires_grad=True) for p in param)
substep_optimizer = "brute"
substep_kwargs = None
opt = RotosolveOptimizer(substep_optimizer, substep_kwargs)
for _ in range(3):
param = opt.step(
fun,
*param,
nums_frequency=num_freq,
)
# The following accounts for the unpacking functionality for length-one param
if len(x_min) == 1:
param = (param, )
assert (np.isscalar(x_min)
and np.isscalar(param)) or len(x_min) == len(param)
assert np.allclose(
np.fromiter(_flatten(x_min), dtype=float),
np.fromiter(_flatten(param), dtype=float),
atol=1e-5,
)
def step(self, objective_fn, x):
r"""Update x with one step of the optimizer.
Args:
objective_fn (function): The objective function for optimization. It should take a
sequence of the values ``x`` and a list of the gates ``generators`` as inputs, and
return a single value.
x (Union[Sequence[float], float]): Sequence containing the initial values of the
variables to be optimized over, or a single float with the initial value.
Returns:
array: The new variable values :math:`x^{(t+1)}`.
"""
x_flat = np.fromiter(_flatten(x), dtype=float)
for d, _ in enumerate(x_flat):
x_flat = self._rotosolve(objective_fn, x_flat, d)
return unflatten(x_flat, x)
def gradient_product(g):
"""Vector-Jacobian product operator.
Args:
g (array[float]): scalar or vector multiplying the Jacobian
from the left (output side)
Returns:
nested Sequence[float]: vector-Jacobian product, arranged
into the nested structure of the input arguments in ``args``
"""
diff_indices = None
non_diff_indices = set()
for arg, arg_variable in zip(args, self.arg_vars):
if not getattr(arg, "requires_grad", True):
indices = [i.idx for i in _flatten(arg_variable)]
non_diff_indices.update(indices)
if non_diff_indices:
diff_indices = set(range(
self.num_variables)) - non_diff_indices
# Jacobian matrix of the circuit
jac = self.jacobian(args, kwargs, wrt=diff_indices)
if not g.shape:
vjp = g * jac # numpy treats 0d arrays as scalars, hence @ cannot be used
else:
vjp = g @ jac
if non_diff_indices:
# Autograd requires we return a gradient of size (num_variables,)
res = zeros([self.num_variables])
indices = fromiter(diff_indices, dtype=int64)
res[indices] = vjp
vjp = res
# Restore the nested structure of the input args.
vjp = unflatten(vjp.flat, args)
return vjp
