完善mps的vidal机制,多节点并行;补充tn搜索时dask集群搜索的方式

tests/test_cpu_backend.py

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+import math
+
+import numpy as np
+from qibo import Circuit, gates, hamiltonians
+from qibo.symbols import X, Z
+
+from qibotn.backends.cpu import CpuTensorNet
+from qibotn.benchmark_cases import build_circuit as build_benchmark_circuit
+
+
+def build_circuit(nqubits=6):
+    circuit = Circuit(nqubits)
+    for qubit in range(nqubits):
+        circuit.add(gates.RY(qubit, theta=0.1 * (qubit + 1)))
+        circuit.add(gates.RZ(qubit, theta=-0.05 * (qubit + 1)))
+    for qubit in range(nqubits - 1):
+        circuit.add(gates.CNOT(qubit, qubit + 1))
+    return circuit
+
+
+def build_observable(nqubits):
+    form = 0
+    for qubit in range(nqubits):
+        form += 0.5 * X(qubit) * Z((qubit + 1) % nqubits)
+    return hamiltonians.SymbolicHamiltonian(form=form)
+
+
+def test_cpu_generic_tn_expectation_matches_statevector():
+    circuit = build_circuit()
+    observable = build_observable(circuit.nqubits)
+    exact = observable.expectation_from_state(circuit().state(numpy=True))
+
+    backend = CpuTensorNet(
+        {
+            "MPI_enabled": False,
+            "MPS_enabled": False,
+            "NCCL_enabled": False,
+            "expectation_enabled": observable,
+        }
+    )
+    value = backend.execute_circuit(circuit)[0]
+
+    assert math.isclose(value, exact, abs_tol=1e-12)
+
+
+def test_cpu_mps_expectation_matches_statevector():
+    circuit = build_circuit()
+    observable = build_observable(circuit.nqubits)
+    exact = observable.expectation_from_state(circuit().state(numpy=True))
+
+    backend = CpuTensorNet(
+        {
+            "MPI_enabled": False,
+            "MPS_enabled": True,
+            "NCCL_enabled": False,
+            "expectation_enabled": observable,
+            "max_bond_dimension": 64,
+            "tensor_module": "torch",
+            "torch_threads": 1,
+        }
+    )
+    value = backend.execute_circuit(circuit)[0]
+
+    assert math.isclose(value, exact, abs_tol=1e-12)
+
+
+def test_cpu_runcard_pauli_pattern_matches_statevector():
+    circuit = build_circuit()
+    observable = {"pauli_string_pattern": "IXZ"}
+    exact_hamiltonian = hamiltonians.SymbolicHamiltonian(
+        form=X(1) * Z(2) * X(4) * Z(5)
+    )
+    exact = exact_hamiltonian.expectation_from_state(circuit().state(numpy=True))
+
+    for mps_enabled in (False, True):
+        backend = CpuTensorNet(
+            {
+                "MPI_enabled": False,
+                "MPS_enabled": mps_enabled,
+                "NCCL_enabled": False,
+                "expectation_enabled": observable,
+                "max_bond_dimension": 64,
+                "tensor_module": "torch",
+                "torch_threads": 1,
+            }
+        )
+        value = backend.execute_circuit(circuit)[0]
+
+        assert math.isclose(value, exact, abs_tol=1e-12)
+
+
+def test_cpu_mps_sampling_uses_nshots():
+    circuit = Circuit(4)
+    circuit.add(gates.H(0))
+    for qubit in range(3):
+        circuit.add(gates.CNOT(qubit, qubit + 1))
+
+    backend = CpuTensorNet(
+        {
+            "MPI_enabled": False,
+            "MPS_enabled": True,
+            "NCCL_enabled": False,
+            "expectation_enabled": False,
+        }
+    )
+    result = backend.execute_circuit(circuit, nshots=100)
+
+    assert sum(result.frequencies().values()) == 100
+    assert set(result.frequencies()) <= {"0000", "1111"}
+
+
+def test_cpu_generic_tn_long_pauli_string_matches_statevector():
+    circuit = build_benchmark_circuit("rxx_rzz", 10, 2, 42)
+    observable = {"pauli_string_pattern": "XZ"}
+    exact_hamiltonian = hamiltonians.SymbolicHamiltonian(
+        form=X(0) * Z(1) * X(2) * Z(3) * X(4) * Z(5) * X(6) * Z(7) * X(8) * Z(9)
+    )
+    exact = exact_hamiltonian.expectation_from_state(circuit().state(numpy=True))
+
+    backend = CpuTensorNet(
+        {
+            "MPI_enabled": False,
+            "MPS_enabled": False,
+            "NCCL_enabled": False,
+            "expectation_enabled": observable,
+        }
+    )
+    value = backend.execute_circuit(circuit)[0]
+
+    assert math.isclose(value, exact, abs_tol=1e-12)