def test_learning_error(self):
"""Test that an exception is raised if the learning rate is beyond the
lipschitz bound"""
coeffs = [0.3, 0.1]
H = qml.Hamiltonian(coeffs, [qml.PauliX(0), qml.PauliZ(0)])
dev = qml.device("default.qubit", wires=1, shots=100)
expval_cost = qml.ExpvalCost(lambda x, **kwargs: qml.RX(x, wires=0), H,
dev)
opt = qml.ShotAdaptiveOptimizer(min_shots=10, stepsize=100.0)
# lipschitz constant is given by sum(|coeffs|)
lipschitz = np.sum(np.abs(coeffs))
assert opt._stepsize > 2 / lipschitz
with pytest.raises(
ValueError,
match=f"The learning rate must be less than {2 / lipschitz}"):
opt.step(expval_cost, 0.5)
# for a single QNode, the lipschitz constant is simply 1
opt = qml.ShotAdaptiveOptimizer(min_shots=10, stepsize=100.0)
with pytest.raises(
ValueError,
match=f"The learning rate must be less than {2 / 1}"):
opt.step(expval_cost.qnodes[0], 0.5)
def test_vqe_optimization(self):
"""Test that a simple VQE circuit can be optimized"""
dev = qml.device("default.qubit", wires=2, shots=100)
coeffs = [0.1, 0.2]
obs = [qml.PauliZ(0), qml.PauliX(0)]
H = qml.Hamiltonian(coeffs, obs)
def ansatz(x, **kwargs):
qml.Rot(*x[0], wires=0)
qml.Rot(*x[1], wires=1)
qml.CNOT(wires=[0, 1])
qml.Rot(*x[2], wires=0)
qml.Rot(*x[3], wires=1)
qml.CNOT(wires=[0, 1])
cost = qml.ExpvalCost(ansatz, H, dev)
params = np.random.random((4, 3), requires_grad=True)
initial_loss = cost(params)
min_shots = 10
loss = initial_loss
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
for i in range(100):
params = opt.step(cost, params)
loss = cost(params)
assert loss < initial_loss
assert np.allclose(loss, -1 / (2 * np.sqrt(5)), atol=0.1, rtol=0.2)
assert opt.shots_used > min_shots
def test_multi_qubit_rotation(self):
"""Test that multiple qubit rotation can be optimized"""
dev = qml.device("default.qubit", wires=2, shots=100)
@qml.qnode(dev)
def circuit(x):
qml.RX(x[0], wires=0)
qml.RY(x[1], wires=1)
qml.RZ(x[2], wires=1)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliX(0) @ qml.PauliY(1))
params = np.array([0.1, 0.3, 0.4], requires_grad=True)
initial_loss = circuit(params)
min_shots = 10
loss = initial_loss
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
for i in range(100):
params = opt.step(circuit, params)
loss = circuit(params)
assert loss < initial_loss
assert np.allclose(circuit(params), -1, atol=0.1, rtol=0.2)
assert opt.shots_used > min_shots
def test_unknown_objective_function(self):
"""Test that an exception is raised if an unknown objective function is passed"""
dev = qml.device("default.qubit", wires=1, shots=100)
@qml.qnode(dev)
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
def cost(x):
return np.sin(circuit(x))
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
# test expval cost
with pytest.raises(
ValueError,
match=
"The objective function must either be encoded as a single QNode"
):
opt.step(cost, 0.5)
# defining the device attribute allows it to proceed
cost.device = circuit.device
new_x = opt.step(cost, 0.5)
assert isinstance(new_x, float)
def test_single_shots(self, mocker, monkeypatch):
"""Test that, if the shot budget for a single term is 1,
that the number of dimensions for the returned Jacobian is expanded"""
coeffs = [0.2, 0.1, 0.1]
dev = qml.device("default.qubit", wires=2, shots=100)
H = qml.Hamiltonian(
coeffs,
[qml.PauliZ(0),
qml.PauliX(1),
qml.PauliZ(0) @ qml.PauliZ(1)])
expval_cost = qml.ExpvalCost(qml.templates.StronglyEntanglingLayers, H,
dev)
weights = qml.init.strong_ent_layers_normal(n_layers=3, n_wires=2)
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
spy = mocker.spy(qml, "jacobian")
spy_dims = mocker.spy(np, "expand_dims")
mocker.patch("scipy.stats._multivariate.multinomial_gen.rvs",
return_value=np.array([[4, 1, 5]]))
grads = opt.weighted_random_sampling(expval_cost.qnodes, coeffs, 10,
[0], weights)
spy_dims.assert_called_once()
assert len(spy.call_args_list) == 3
assert len(grads) == 1
assert grads[0].shape == (10, *weights.shape)
def test_single_argument_step(self, cost_fn, mocker, monkeypatch):
"""Test that a simple QNode with a single argument correctly performs an optimization step,
and that the single-shot gradients generated have the correct shape"""
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
spy_single_shot_expval = mocker.spy(opt,
"_single_shot_expval_gradients")
spy_single_shot_qnodes = mocker.spy(opt,
"_single_shot_qnode_gradients")
spy_grad = mocker.spy(opt, "compute_grad")
x_init = 0.5
new_x = opt.step(cost_fn, x_init)
assert isinstance(new_x, float)
assert new_x != x_init
spy_grad.assert_called_once()
if isinstance(cost_fn, qml.ExpvalCost):
spy_single_shot_expval.assert_called_once()
single_shot_grads = opt._single_shot_expval_gradients(
cost_fn, [x_init], {})
else:
spy_single_shot_qnodes.assert_called_once()
single_shot_grads = opt._single_shot_qnode_gradients(
cost_fn, [x_init], {})
# assert single shot gradients are computed correctly
assert len(single_shot_grads) == 1
assert single_shot_grads[0].shape == (10, )
# monkeypatch the optimizer to use the same single shot gradients
# as previously
monkeypatch.setattr(opt, "_single_shot_qnode_gradients",
lambda *args, **kwargs: single_shot_grads)
monkeypatch.setattr(opt, "_single_shot_expval_gradients",
lambda *args, **kwargs: single_shot_grads)
# reset the shot budget
opt.s = [np.array(10)]
# check that the gradient and variance are computed correctly
grad, grad_variance = opt.compute_grad(cost_fn, [x_init], {})
assert len(grad) == 1
assert len(grad_variance) == 1
assert np.allclose(grad, np.mean(single_shot_grads))
assert np.allclose(grad_variance, np.var(single_shot_grads, ddof=1))
# check that the gradient and variance are computed correctly
# with a different shot budget
opt.s = [np.array(5)]
grad, grad_variance = opt.compute_grad(cost_fn, [x_init], {})
assert len(grad) == 1
assert len(grad_variance) == 1
assert np.allclose(grad, np.mean(single_shot_grads[0][:5]))
assert np.allclose(grad_variance,
np.var(single_shot_grads[0][:5], ddof=1))
def test_multiple_argument_step(self, mocker, monkeypatch):
"""Test that a simple QNode with multiple scalar arguments correctly performs an optimization step,
and that the single-shot gradients generated have the correct shape"""
dev = qml.device("default.qubit", wires=1, shots=100)
@qml.qnode(dev)
def circuit(x, y):
qml.RX(x, wires=0)
qml.RY(y, wires=0)
return qml.expval(qml.PauliZ(0))
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
spy_single_shot = mocker.spy(opt, "_single_shot_qnode_gradients")
spy_grad = mocker.spy(opt, "compute_grad")
args = [0.1, 0.2]
new_x = opt.step(circuit, *args)
assert isinstance(new_x, list)
assert len(new_x) == 2
spy_single_shot.assert_called_once()
spy_grad.assert_called_once()
# assert single shot gradients are computed correctly
single_shot_grads = opt._single_shot_qnode_gradients(circuit, args, {})
assert len(single_shot_grads) == 2
assert single_shot_grads[0].shape == (10, )
# monkeypatch the optimizer to use the same single shot gradients
# as previously
monkeypatch.setattr(opt, "_single_shot_qnode_gradients",
lambda *args, **kwargs: single_shot_grads)
# reset the shot budget
opt.s = [np.array(10), np.array(10)]
# check that the gradient and variance are computed correctly
grad, grad_variance = opt.compute_grad(circuit, args, {})
assert len(grad) == 2
assert len(grad_variance) == 2
assert np.allclose(grad, np.mean(single_shot_grads, axis=1))
assert np.allclose(grad_variance,
np.var(single_shot_grads, ddof=1, axis=1))
# check that the gradient and variance are computed correctly
# with a different shot budget
opt.s = [np.array(5), np.array(7)]
grad, grad_variance = opt.compute_grad(circuit, args, {})
assert len(grad) == 2
assert len(grad_variance) == 2
for p, s in zip(range(2), opt.s):
assert np.allclose(grad[p], np.mean(single_shot_grads[p][:s]))
assert np.allclose(grad_variance[p],
np.var(single_shot_grads[p][:s], ddof=1))
def test_unknown_term_sampling_method(self):
"""Checks that an exception is raised if the term sampling method is unknown"""
coeffs = [0.2, 0.1]
dev = qml.device("default.qubit", wires=2, shots=100)
H = qml.Hamiltonian(coeffs, [qml.PauliZ(0), qml.PauliZ(0) @ qml.PauliZ(1)])
expval_cost = qml.ExpvalCost(qml.templates.StronglyEntanglingLayers, H, dev)
weights = qml.init.strong_ent_layers_normal(n_layers=3, n_wires=2)
opt = qml.ShotAdaptiveOptimizer(min_shots=10, term_sampling="uniform_random_sampling")
with pytest.raises(ValueError, match="Unknown Hamiltonian term sampling method"):
opt.step(expval_cost, weights)
def test_wrs_disabled(self, mocker):
"""Checks that cost functions that are expval costs can
disable use of weighted random sampling"""
coeffs = [0.2, 0.1]
dev = qml.device("default.qubit", wires=2, shots=100)
H = qml.Hamiltonian(coeffs, [qml.PauliZ(0), qml.PauliZ(0) @ qml.PauliZ(1)])
expval_cost = qml.ExpvalCost(qml.templates.StronglyEntanglingLayers, H, dev)
weights = qml.init.strong_ent_layers_normal(n_layers=3, n_wires=2)
opt = qml.ShotAdaptiveOptimizer(min_shots=10, term_sampling=None)
spy = mocker.spy(opt, "weighted_random_sampling")
new_weights = opt.step(expval_cost, weights)
spy.assert_not_called()
def test_analytic_device_error(self):
"""Test that an exception is raised if an analytic device is used"""
H = qml.Hamiltonian([0.3, 0.1], [qml.PauliX(0), qml.PauliZ(0)])
dev = qml.device("default.qubit", wires=1, shots=None)
expval_cost = qml.ExpvalCost(lambda x, **kwargs: qml.RX(x, wires=0), H, dev)
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
# test expval cost
with pytest.raises(ValueError, match="can only be used with devices that"):
opt.step(expval_cost, 0.5)
# test qnode cost
with pytest.raises(ValueError, match="can only be used with devices that"):
opt.step(expval_cost.qnodes[0], 0.5)
def test_expval_cost(self, tol):
"""Test that the cost is correctly returned
when using a QNode as the cost function"""
dev = qml.device("default.qubit", wires=1, shots=10)
def ansatz(x, **kwargs):
qml.RX(x[0], wires=0)
qml.RY(x[1], wires=0)
H = qml.Hamiltonian([1.0], [qml.PauliZ(0)])
circuit = qml.ExpvalCost(ansatz, H, dev)
params = np.array([0.1, 0.3], requires_grad=True)
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
for i in range(100):
params, res = opt.step_and_cost(circuit, params)
assert np.allclose(res, -1, atol=tol, rtol=0)
def test_qnode_cost(self, tol):
"""Test that the cost is correctly returned
when using a QNode as the cost function"""
dev = qml.device("default.qubit", wires=1, shots=10)
@qml.qnode(dev)
def circuit(x):
qml.RX(x[0], wires=0)
qml.RY(x[1], wires=0)
return qml.expval(qml.PauliZ(0))
params = np.array([0.1, 0.3], requires_grad=True)
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
for i in range(100):
params, res = opt.step_and_cost(circuit, params)
assert np.allclose(res, -1, atol=tol, rtol=0)
def test_wrs_expval_cost(self, mocker):
"""Checks that cost functions that are expval costs can
make use of weighted random sampling"""
coeffs = [0.2, 0.1]
dev = qml.device("default.qubit", wires=2, shots=100)
H = qml.Hamiltonian(coeffs, [qml.PauliZ(0), qml.PauliZ(0) @ qml.PauliZ(1)])
expval_cost = qml.ExpvalCost(qml.templates.StronglyEntanglingLayers, H, dev)
weights = qml.init.strong_ent_layers_normal(n_layers=3, n_wires=2)
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
spy = mocker.spy(opt, "weighted_random_sampling")
new_weights = opt.step(expval_cost, weights)
spy.assert_called_once()
grads = opt.weighted_random_sampling(expval_cost.qnodes, coeffs, 10, [0], weights)
assert len(grads) == 1
assert grads[0].shape == (10, *weights.shape)
def test_zero_shots(self, mocker, monkeypatch):
"""Test that, if the shot budget for a single term is 0,
that the jacobian computation is skipped"""
coeffs = [0.2, 0.1, 0.1]
dev = qml.device("default.qubit", wires=2, shots=100)
H = qml.Hamiltonian(coeffs, [qml.PauliZ(0), qml.PauliX(1), qml.PauliZ(0) @ qml.PauliZ(1)])
expval_cost = qml.ExpvalCost(qml.templates.StronglyEntanglingLayers, H, dev)
weights = np.random.random(qml.templates.StronglyEntanglingLayers.shape(3, 2))
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
spy = mocker.spy(qml, "jacobian")
mocker.patch(
"scipy.stats._multivariate.multinomial_gen.rvs", return_value=np.array([[4, 0, 6]])
)
grads = opt.weighted_random_sampling(expval_cost.qnodes, coeffs, 10, [0], weights)
assert len(spy.call_args_list) == 2
assert len(grads) == 1
assert grads[0].shape == (10, *weights.shape)
def test_multiple_array_argument_step(self, mocker, monkeypatch):
"""Test that a simple QNode with multiple array arguments correctly performs an optimization step,
and that the single-shot gradients generated have the correct shape"""
dev = qml.device("default.qubit", wires=1, shots=100)
@qml.qnode(dev)
def circuit(x, y):
qml.RX(x[0, 0], wires=0)
qml.RY(x[0, 1], wires=0)
qml.RZ(x[0, 2], wires=0)
qml.RX(x[1, 0], wires=0)
qml.RY(x[1, 1], wires=0)
qml.RZ(x[1, 2], wires=0)
qml.RX(y[0], wires=0)
qml.RY(y[1], wires=0)
qml.RZ(y[2], wires=0)
return qml.expval(qml.PauliZ(0))
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
spy_single_shot = mocker.spy(opt, "_single_shot_qnode_gradients")
spy_grad = mocker.spy(opt, "compute_grad")
args = [np.array([[1., 2., 3.], [4., 5., 6.]]), np.array([1., 2., 3.])]
new_x = opt.step(circuit, *args)
assert isinstance(new_x, list)
assert len(new_x) == 2
spy_single_shot.assert_called_once()
spy_grad.assert_called_once()
# assert single shot gradients are computed correctly
single_shot_grads = opt._single_shot_qnode_gradients(circuit, args, {})
assert len(single_shot_grads) == 2
assert single_shot_grads[0].shape == (10, 2, 3)
assert single_shot_grads[1].shape == (10, 3)
# monkeypatch the optimizer to use the same single shot gradients
# as previously
monkeypatch.setattr(opt, "_single_shot_qnode_gradients", lambda *args, **kwargs: single_shot_grads)
# reset the shot budget
opt.s = [10 * np.ones([2, 3], dtype=np.int64), 10 * np.ones([3], dtype=np.int64)]
# check that the gradient and variance are computed correctly
grad, grad_variance = opt.compute_grad(circuit, args, {})
assert len(grad) == 2
assert len(grad_variance) == 2
for i in range(2):
assert grad[i].shape == args[i].shape
assert grad_variance[i].shape == args[i].shape
assert np.allclose(grad[0], np.mean(single_shot_grads[0], axis=0))
assert np.allclose(grad_variance[0], np.var(single_shot_grads[0], ddof=1, axis=0))
assert np.allclose(grad[1], np.mean(single_shot_grads[1], axis=0))
assert np.allclose(grad_variance[1], np.var(single_shot_grads[1], ddof=1, axis=0))
# check that the gradient and variance are computed correctly
# with a different shot budget
opt.s[0] = opt.s[0] // 2 # all array elements have a shot budget of 5
opt.s[0][0, 0] = 8 # set the shot budget of the zeroth element to 8
opt.s[1] = opt.s[1] // 5 # all array elements have a shot budget of 2
opt.s[1][0] = 7 # set the shot budget of the zeroth element to 7
grad, grad_variance = opt.compute_grad(circuit, args, {})
assert len(grad) == 2
assert len(grad_variance) == 2
# check zeroth element of arg 0
assert np.allclose(grad[0][0, 0], np.mean(single_shot_grads[0][:8, 0, 0]))
assert np.allclose(grad_variance[0][0, 0], np.var(single_shot_grads[0][:8, 0, 0], ddof=1))
# check other elements of arg 0
assert np.allclose(grad[0][0, 1], np.mean(single_shot_grads[0][:5, 0, 1]))
assert np.allclose(grad_variance[0][0, 1], np.var(single_shot_grads[0][:5, 0, 1], ddof=1))
# check zeroth element of arg 1
assert np.allclose(grad[1][0], np.mean(single_shot_grads[1][:7, 0]))
assert np.allclose(grad_variance[1][0], np.var(single_shot_grads[1][:7, 0], ddof=1))
# check other elements of arg 1
assert np.allclose(grad[1][1], np.mean(single_shot_grads[1][:2, 1]))
assert np.allclose(grad_variance[1][1], np.var(single_shot_grads[1][:2, 1], ddof=1))
def test_single_array_argument_step(self, cost_fn, mocker, monkeypatch):
"""Test that a simple QNode with a single array argument correctly performs an optimization step,
and that the single-shot gradients generated have the correct shape"""
opt = qml.ShotAdaptiveOptimizer(min_shots=10)
spy_single_shot_expval = mocker.spy(opt, "_single_shot_expval_gradients")
spy_single_shot_qnodes = mocker.spy(opt, "_single_shot_qnode_gradients")
spy_grad = mocker.spy(opt, "compute_grad")
x_init = np.array([[1., 2., 3.], [4., 5., 6.]])
new_x = opt.step(cost_fn, x_init)
assert isinstance(new_x, np.ndarray)
assert not np.allclose(new_x, x_init)
if isinstance(cost_fn, qml.ExpvalCost):
spy_single_shot_expval.assert_called_once()
single_shot_grads = opt._single_shot_expval_gradients(cost_fn, [x_init], {})
else:
spy_single_shot_qnodes.assert_called_once()
single_shot_grads = opt._single_shot_qnode_gradients(cost_fn, [x_init], {})
spy_grad.assert_called_once()
# assert single shot gradients are computed correctly
assert len(single_shot_grads) == 1
assert single_shot_grads[0].shape == (10, 2, 3)
# monkeypatch the optimizer to use the same single shot gradients
# as previously
monkeypatch.setattr(opt, "_single_shot_qnode_gradients", lambda *args, **kwargs: single_shot_grads)
monkeypatch.setattr(opt, "_single_shot_expval_gradients", lambda *args, **kwargs: single_shot_grads)
# reset the shot budget
opt.s = [10 * np.ones([2, 3], dtype=np.int64)]
# check that the gradient and variance are computed correctly
grad, grad_variance = opt.compute_grad(cost_fn, [x_init], {})
assert len(grad) == 1
assert len(grad_variance) == 1
assert grad[0].shape == x_init.shape
assert grad_variance[0].shape == x_init.shape
assert np.allclose(grad, np.mean(single_shot_grads, axis=1))
assert np.allclose(grad_variance, np.var(single_shot_grads, ddof=1, axis=1))
# check that the gradient and variance are computed correctly
# with a different shot budget
opt.s[0] = opt.s[0] // 2 # all array elements have a shot budget of 5
opt.s[0][0, 0] = 8 # set the shot budget of the zeroth element to 8
grad, grad_variance = opt.compute_grad(cost_fn, [x_init], {})
assert len(grad) == 1
assert len(grad_variance) == 1
# check zeroth element
assert np.allclose(grad[0][0, 0], np.mean(single_shot_grads[0][:8, 0, 0]))
assert np.allclose(grad_variance[0][0, 0], np.var(single_shot_grads[0][:8, 0, 0], ddof=1))
# check other elements
assert np.allclose(grad[0][0, 1], np.mean(single_shot_grads[0][:5, 0, 1]))
assert np.allclose(grad_variance[0][0, 1], np.var(single_shot_grads[0][:5, 0, 1], ddof=1))
