Rewrite test functions into higher level

tests/test_cuquantum_cutensor_backend.py

@@ -1,11 +1,8 @@
 import math
-from timeit import default_timer as timer
-
 import cupy as cp
-import numpy as np
 import pytest
 import qibo
-from qibo import Circuit, construct_backend, gates, hamiltonians
+from qibo import construct_backend, hamiltonians
 from qibo.models import QFT
 from qibo.symbols import X, Z
 
@@ -16,14 +13,6 @@ def qibo_qft(nqubits, swaps):
     return circ_qibo, state_vec
 
 
-def time(func):
-    start = timer()
-    res = func()
-    end = timer()
-    time = end - start
-    return time, res
-
-
 def build_observable(nqubits):
     """Helper function to construct a target observable."""
     hamiltonian_form = 0
@@ -34,35 +23,64 @@ def build_observable(nqubits):
     return hamiltonian, hamiltonian_form
 
 
-@pytest.mark.gpu
+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.parametrize("nqubits", [1, 2, 5, 10])
 def test_eval(nqubits: int, dtype="complex128"):
-    """Evaluate QASM 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.
     """
-    import qibotn.eval
-
     # Test qibo
-    # qibo.set_backend(backend=config.qibo.backend, platform=config.qibo.platform)
     qibo.set_backend(backend="numpy")
-    qibo_time, (qibo_circ, result_sv) = time(lambda: qibo_qft(nqubits, swaps=True))
+    qibo_circ, result_sv = qibo_qft(nqubits, swaps=True)
     result_sv_cp = cp.asarray(result_sv)
 
-    # Test Cuquantum
-    cutn_time, result_tn = time(
-        lambda: qibotn.eval.dense_vector_tn(qibo_circ, dtype).flatten()
+    # Test cutensornet
+    backend = construct_backend(backend="qibotn", platform="cutensornet")
+    # Test 1: no computation settings specified. Use default.
+    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"
 
-    print(f"State vector difference: {abs(result_tn - result_sv_cp).max():0.3e}")
-
-    assert cp.allclose(result_sv_cp, result_tn), "Resulting dense vectors do not match"
+    # Test 2: Explicit computation settings specified (same as default).
+    computation_settings = {
+        "MPI_enabled": False,
+        "MPS_enabled": False,
+        "NCCL_enabled": False,
+        "expectation_enabled": False,
+    }
+    backend.configure_tn_simulation(computation_settings)
+    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.
@@ -72,41 +90,59 @@ def test_mps(nqubits: int, dtype="complex128"):
         dtype (str): The data type for precision, 'complex64' for single,
             'complex128' for double.
     """
-    import qibotn.eval
 
     # Test qibo
     qibo.set_backend(backend="numpy")
-
-    qibo_time, (circ_qibo, result_sv) = time(lambda: qibo_qft(nqubits, swaps=True))
-
+    qibo_circ, result_sv = qibo_qft(nqubits, swaps=True)
     result_sv_cp = cp.asarray(result_sv)
 
-    # Test of MPS
-    gate_algo = {
-        "qr_method": False,
-        "svd_method": {
-            "partition": "UV",
-            "abs_cutoff": 1e-12,
-        },
+    # Test cutensornet
+    backend = construct_backend(backend="qibotn", platform="cutensornet")
+    # Test 1: No MPS computation settings specified. Use default.
+    computation_settings_1 = {
+        "MPI_enabled": False,
+        "MPS_enabled": True,
+        "NCCL_enabled": False,
+        "expectation_enabled": False,
     }
-
-    cutn_time, result_tn = time(
-        lambda: qibotn.eval.dense_vector_mps(circ_qibo, gate_algo, dtype).flatten()
+    backend.configure_tn_simulation(computation_settings_1)
+    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"
 
-    print(f"State vector difference: {abs(result_tn - result_sv_cp).max():0.3e}")
-
-    assert cp.allclose(result_tn, result_sv_cp), "Resulting dense vectors do not match"
+    # Test 2: Explicit MPS computation settings specified (same as default).
+    computation_settings_2 = {
+        "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(computation_settings_2)
+    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.gpu
 @pytest.mark.parametrize("nqubits", [2, 5, 10])
 def test_expectation(nqubits: int, dtype="complex128"):
-    import qibotn.eval
 
-    circ_qibo, state_vec_qibo = qibo_qft(nqubits, swaps=True)
+    # 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,
@@ -114,6 +150,39 @@ def test_expectation(nqubits: int, dtype="complex128"):
         normalize=False,
     )
 
-    tn_expval = qibotn.eval.expectation_tn(circ_qibo, dtype, ham).flatten()
+    # Test cutensornet
+    backend = construct_backend(backend="qibotn", platform="cutensornet")
 
-    assert math.isclose(exact_expval.item(), tn_expval.real.get().item(), abs_tol=1e-7)
+    # Test 1: No Hamilitonian computation settings specified. Use default.
+    computation_settings_1 = {
+        "MPI_enabled": False,
+        "MPS_enabled": False,
+        "NCCL_enabled": False,
+        "expectation_enabled": True,
+    }
+    backend.configure_tn_simulation(computation_settings_1)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    assert math.isclose(exact_expval.item(), result_tn.real.get().item(), abs_tol=1e-7)
+
+    # Test 2: hamiltonians.SymbolicHamiltonian object in computation settings specified.
+    computation_settings_2 = {
+        "MPI_enabled": False,
+        "MPS_enabled": False,
+        "NCCL_enabled": False,
+        "expectation_enabled": ham,
+    }
+    backend.configure_tn_simulation(computation_settings_2)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    assert math.isclose(exact_expval.item(), result_tn.real.get().item(), abs_tol=1e-7)
+
+    # Test 3: Dictionary object form of hamiltonian in computation settings specified.
+    ham_dict = build_observable_dict(nqubits)
+    computation_settings_3 = {
+        "MPI_enabled": False,
+        "MPS_enabled": False,
+        "NCCL_enabled": False,
+        "expectation_enabled": ham_dict,
+    }
+    backend.configure_tn_simulation(computation_settings_3)
+    result_tn = backend.execute_circuit(circuit=qibo_circ)
+    assert math.isclose(exact_expval.item(), result_tn.real.get().item(), abs_tol=1e-7)