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tests/test_cuquantum_cutensor_backend.py

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+import math
+
+import cupy as cp
+import pytest
+import qibo
+from qibo import construct_backend, hamiltonians
+from qibo.models import QFT
+from qibo.symbols import X, Z
+
+ABS_TOL = 1e-7
+
+
+def qibo_qft(nqubits, swaps):
+    circ_qibo = QFT(nqubits, swaps)
+    state_vec = circ_qibo().state(numpy=True)
+    return circ_qibo, state_vec
+
+
+def build_observable(nqubits):
+    """Helper function to construct a target observable."""
+    hamiltonian_form = 0
+    for i in range(nqubits):
+        hamiltonian_form += 0.5 * X(i % nqubits) * Z((i + 1) % nqubits)
+
+    hamiltonian = hamiltonians.SymbolicHamiltonian(form=hamiltonian_form)
+    return hamiltonian, hamiltonian_form
+
+
+def build_observable_dict(nqubits):
+    """Construct a target observable as a dictionary representation.
+
+    Returns a dictionary suitable for `create_hamiltonian_from_dict`.
+    """
+    terms = []
+
+    for i in range(nqubits):
+        term = {
+            "coefficient": 0.5,
+            "operators": [("X", i % nqubits), ("Z", (i + 1) % nqubits)],
+        }
+        terms.append(term)
+
+    return {"terms": terms}
+
+
+@pytest.mark.gpu
+@pytest.mark.parametrize("nqubits", [1, 2, 5, 10])
+def test_eval(nqubits: int, dtype="complex128"):
+    """
+    Args:
+        nqubits (int): Total number of qubits in the system.
+        dtype (str): The data type for precision, 'complex64' for single,
+            'complex128' for double.
+    """
+    # Test qibo
+    qibo.set_backend(backend="numpy")
+    qibo_circ, result_sv = qibo_qft(nqubits, swaps=True)
+    result_sv_cp = cp.asarray(result_sv)
+
+    # Test cutensornet
+    backend = construct_backend(backend="qibotn", platform="cutensornet")
+    # Test with no settings specified. Default is dense vector calculation without MPI or NCCL.
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    print(
+        f"State vector difference: {abs(result_tn.statevector.flatten() - result_sv_cp).max():0.3e}"
+    )
+    assert cp.allclose(
+        result_sv_cp, result_tn.statevector.flatten()
+    ), "Resulting dense vectors do not match"
+
+    # Test with explicit settings specified.
+    comp_set_w_bool = {
+        "MPI_enabled": False,
+        "MPS_enabled": False,
+        "NCCL_enabled": False,
+        "expectation_enabled": False,
+    }
+    backend.configure_tn_simulation(comp_set_w_bool)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    print(
+        f"State vector difference: {abs(result_tn.statevector.flatten() - result_sv_cp).max():0.3e}"
+    )
+    assert cp.allclose(
+        result_sv_cp, result_tn.statevector.flatten()
+    ), "Resulting dense vectors do not match"
+
+
+@pytest.mark.gpu
+@pytest.mark.parametrize("nqubits", [2, 5, 10])
+def test_mps(nqubits: int, dtype="complex128"):
+    """Evaluate MPS with cuQuantum.
+
+    Args:
+        nqubits (int): Total number of qubits in the system.
+        dtype (str): The data type for precision, 'complex64' for single,
+            'complex128' for double.
+    """
+
+    # Test qibo
+    qibo.set_backend(backend="numpy")
+    qibo_circ, result_sv = qibo_qft(nqubits, swaps=True)
+    result_sv_cp = cp.asarray(result_sv)
+
+    # Test cutensornet
+    backend = construct_backend(backend="qibotn", platform="cutensornet")
+    # Test with simple MPS settings specified using bool. Uses the default MPS parameters.
+    comp_set_w_bool = {
+        "MPI_enabled": False,
+        "MPS_enabled": True,
+        "NCCL_enabled": False,
+        "expectation_enabled": False,
+    }
+    backend.configure_tn_simulation(comp_set_w_bool)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    print(
+        f"State vector difference: {abs(result_tn.statevector.flatten() - result_sv_cp).max():0.3e}"
+    )
+    assert cp.allclose(
+        result_tn.statevector.flatten(), result_sv_cp
+    ), "Resulting dense vectors do not match"
+
+    # Test with explicit MPS computation settings specified using Dict. Users able to specify parameters like qr_method etc.
+    comp_set_w_MPS_config_para = {
+        "MPI_enabled": False,
+        "MPS_enabled": {
+            "qr_method": False,
+            "svd_method": {
+                "partition": "UV",
+                "abs_cutoff": 1e-12,
+            },
+        },
+        "NCCL_enabled": False,
+        "expectation_enabled": False,
+    }
+    backend.configure_tn_simulation(comp_set_w_MPS_config_para)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    print(
+        f"State vector difference: {abs(result_tn.statevector.flatten() - result_sv_cp).max():0.3e}"
+    )
+    assert cp.allclose(
+        result_tn.statevector.flatten(), result_sv_cp
+    ), "Resulting dense vectors do not match"
+
+
+@pytest.mark.parametrize("nqubits", [2, 5, 10])
+def test_expectation(nqubits: int, dtype="complex128"):
+
+    # Test qibo
+    qibo_circ, state_vec_qibo = qibo_qft(nqubits, swaps=True)
+    ham, ham_form = build_observable(nqubits)
+    numpy_backend = construct_backend("numpy")
+    exact_expval = numpy_backend.calculate_expectation_state(
+        hamiltonian=ham,
+        state=state_vec_qibo,
+        normalize=False,
+    )
+
+    # Test cutensornet
+    backend = construct_backend(backend="qibotn", platform="cutensornet")
+
+    # Test with simple settings using bool. Uses default Hamilitonian for expectation calculation.
+    comp_set_w_bool = {
+        "MPI_enabled": False,
+        "MPS_enabled": False,
+        "NCCL_enabled": False,
+        "expectation_enabled": True,
+    }
+    backend.configure_tn_simulation(comp_set_w_bool)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    assert math.isclose(
+        exact_expval.item(), result_tn.real.get().item(), abs_tol=ABS_TOL
+    )
+
+    # Test with user defined hamiltonian using "hamiltonians.SymbolicHamiltonian" object.
+    comp_set_w_hamiltonian_obj = {
+        "MPI_enabled": False,
+        "MPS_enabled": False,
+        "NCCL_enabled": False,
+        "expectation_enabled": ham,
+    }
+    backend.configure_tn_simulation(comp_set_w_hamiltonian_obj)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    assert math.isclose(
+        exact_expval.item(), result_tn.real.get().item(), abs_tol=ABS_TOL
+    )
+
+    # Test with user defined hamiltonian using Dictionary object form of hamiltonian.
+    ham_dict = build_observable_dict(nqubits)
+    comp_set_w_hamiltonian_dict = {
+        "MPI_enabled": False,
+        "MPS_enabled": False,
+        "NCCL_enabled": False,
+        "expectation_enabled": ham_dict,
+    }
+    backend.configure_tn_simulation(comp_set_w_hamiltonian_dict)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    assert math.isclose(
+        exact_expval.item(), result_tn.real.get().item(), abs_tol=ABS_TOL
+    )