Update dependency pulser-core to v1.2.0 (#636)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>

pyproject.toml

@@ -62,8 +62,8 @@ allow-ambiguous-features = true
 
 [project.optional-dependencies]
 pulser = [
-    "pulser-core==1.1.1",
-    "pulser-simulation==1.1.1",
+    "pulser-core==1.2.0",
+    "pulser-simulation==1.2.0",
     "pasqal-cloud==0.12.6",
 ]
 visualization = [

qadence/backends/pulser/backend.py

@@ -259,7 +259,7 @@ def run_noisy_sim(noise_prob: float) -> Tensor:
             for i, param_values_el in enumerate(vals):
                 sequence = self.assign_parameters(circuit, param_values_el)
                 sim_result: CoherentResults = simulate_sequence(sequence, self.config, state)
-                final_state = sim_result.get_final_state().data.toarray()
+                final_state = sim_result.get_final_state().data.to_array()
                 batched_dm[i] = np.flip(final_state)
             return torch.from_numpy(batched_dm)
 

qadence/engines/torch/differentiable_expectation.py

@@ -49,7 +49,9 @@ def forward(
             if isinstance(expectation_values[0], list):
                 exp_vals: list = []
                 for expectation_value in expectation_values:
-                    res = list(map(lambda x: x.get_final_state().data.toarray(), expectation_value))
+                    res = list(
+                        map(lambda x: x.get_final_state().data.to_array(), expectation_value)
+                    )
                     exp_vals.append(torch.tensor(res))
                 expectation_values = exp_vals
             return torch.stack(expectation_values)

tests/backends/pulser_basic/test_quantum_pulser.py

@@ -71,6 +71,14 @@ def test_noisy_simulations(noiseless_pulser_sim: Tensor, noisy_pulser_sim: Tenso
     assert torch.allclose(noiseless_expectation, noiseless_pulser_sim, atol=1.0e-3)
     assert torch.allclose(noisy_expectation, noisy_pulser_sim, atol=1.0e-3)
 
+    # test backend itself
+    backend = backend_factory(backend=BackendName.PULSER, diff_mode=DiffMode.GPSR)
+    (pulser_circ, pulser_obs, embed, params) = backend.convert(circuit, observable)
+    native_noisy_expectation = backend.expectation(
+        pulser_circ, pulser_obs, embed(params, {}), noise=noise
+    )
+    assert torch.allclose(native_noisy_expectation, noisy_expectation, atol=1.0e-3)
+
 
 def test_batched_noisy_simulations(
     noiseless_pulser_sim: Tensor, batched_noisy_pulser_sim: Tensor
@@ -95,3 +103,11 @@ def test_batched_noisy_simulations(
     batched_noisy_expectation = model_noisy.expectation()
     assert torch.allclose(noiseless_expectation, noiseless_pulser_sim, atol=1.0e-3)
     assert torch.allclose(batched_noisy_expectation, batched_noisy_pulser_sim, atol=1.0e-3)
+
+    # test backend itself
+    backend = backend_factory(backend=BackendName.PULSER, diff_mode=DiffMode.GPSR)
+    (pulser_circ, pulser_obs, embed, params) = backend.convert(circuit, observable)
+    batched_native_expectation = backend.expectation(
+        pulser_circ, pulser_obs, embed(params, {}), noise=noise
+    )
+    assert torch.allclose(batched_native_expectation, batched_noisy_pulser_sim, atol=1.0e-3)