Skip to content

use lightning in quantumvolume; fix qnspsa for new return; use H.sparse_matrix #757

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
timmysilv merged 5 commits into from
Apr 27, 2023
Merged

use lightning in quantumvolume; fix qnspsa for new return; use H.sparse_matrix #757

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
41e95a5
use lightning in quantumvolume; fix qnspsa for new return
timmysilv Apr 24, 2023
aa72981
Merge branch 'dev' into timmysilv/0.30-updates
timmysilv Apr 24, 2023
c74153d
stop using sparse_hamiltonian
timmysilv Apr 26, 2023
ddcfd35
Merge branch 'dev' into timmysilv/0.30-updates
timmysilv Apr 26, 2023
d6c591e
Merge branch 'dev' into timmysilv/0.30-updates
timmysilv Apr 27, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions demonstrations/qnspsa.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -383,7 +383,7 @@ def get_overlap_tape(qnode, params1, params2):


def get_state_overlap(tape):
return qml.execute([tape], dev, None)[0][0][0]
return qml.execute([tape], dev, None)[0][0]


######################################################################
Expand Down Expand Up @@ -810,10 +810,10 @@ def __post_process_tensor(self, tensor_raw_results, tensor_dirs):
# is a tensor of shape (d x d), d being the dimension of the input parameter
# to the ansatz.
tensor_finite_diff = (
tensor_raw_results[0][0][0]
- tensor_raw_results[1][0][0]
- tensor_raw_results[2][0][0]
+ tensor_raw_results[3][0][0]
tensor_raw_results[0][0]
- tensor_raw_results[1][0]
- tensor_raw_results[2][0]
+ tensor_raw_results[3][0]
)
metric_tensor = (
-(
Expand Down
2 changes: 1 addition & 1 deletion demonstrations/quantum_volume.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -378,7 +378,7 @@ def qv_circuit_layer(num_qubits):
# need to run the same random circuit on two devices independently.

num_qubits = 5
dev_ideal = qml.device("default.qubit", shots=None, wires=num_qubits)
dev_ideal = qml.device("lightning.qubit", shots=None, wires=num_qubits)

m = 3 # number of qubits

Expand Down
4 changes: 2 additions & 2 deletions demonstrations/tutorial_ml_classical_shadows.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -168,7 +168,7 @@ def corr_function(i, j):
import scipy as sp

ham = Hamiltonian(J_mat)
eigvals, eigvecs = sp.sparse.linalg.eigs(qml.utils.sparse_hamiltonian(ham))
eigvals, eigvecs = sp.sparse.linalg.eigs(ham.sparse_matrix())
psi0 = eigvecs[:, np.argmin(eigvals)]


Expand Down Expand Up @@ -516,7 +516,7 @@ def build_dataset(num_points, Nr, Nc, T=500):

for coupling_mat in coupling_mats:
ham = Hamiltonian(coupling_mat)
eigvals, eigvecs = sp.sparse.linalg.eigs(qml.utils.sparse_hamiltonian(ham))
eigvals, eigvecs = sp.sparse.linalg.eigs(ham.sparse_matrix())
psi = eigvecs[:, np.argmin(eigvals)]
shadow = gen_class_shadow(circuit_oshot, psi, T, num_qubits)

Expand Down