期望值计算支持；更新后端调用

benchmark_expectation.py

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+"""Expectation value benchmark with timing breakdown."""
+import time
+import numpy as np
+import torch
+import qibo
+from qibo import Circuit, gates, hamiltonians
+from qibo.symbols import X, Z
+import quimb as qu
+from qibotn.parallel import parallel_path_search
+
+# Config
+NQUBITS = 25
+NLAYERS = 10
+WORKERS = 96
+
+# Build circuit
+np.random.seed(42)
+circuit = Circuit(NQUBITS)
+for _ in range(NLAYERS):
+    for q in range(NQUBITS):
+        circuit.add(gates.RY(q, theta=np.random.uniform(0, 2*np.pi)))
+        circuit.add(gates.RZ(q, theta=np.random.uniform(0, 2*np.pi)))
+    for q in range(NQUBITS):
+        circuit.add(gates.CNOT(q % NQUBITS, (q + 1) % NQUBITS))
+
+# Setup backend
+qibo.set_backend("qibotn", platform="quimb")
+backend = qibo.get_backend()
+backend.configure_tn_simulation(ansatz="tn")
+torch.set_num_threads(WORKERS)
+
+# Prepare TN
+t0 = time.time()
+qc = backend._qibo_circuit_to_quimb(circuit, backend.circuit_ansatz)
+op = qu.pauli('x') & qu.pauli('z')
+tn = qc.local_expectation(op, (0, 1), rehearse='tn')
+t_prepare = time.time() - t0
+
+# Search path
+t0 = time.time()
+tree = parallel_path_search(tn, tn.outer_inds(), 'processpool', 1024, 300, WORKERS)
+t_search = time.time() - t0
+
+# Contract
+t0 = time.time()
+for tensor in tn.tensors:
+    tensor._data = torch.from_numpy(np.asarray(tensor._data)).to(torch.complex128)
+val = tn.contract(all, output_inds=(), optimize=tree)
+t_contract = time.time() - t0
+
+# Results
+print(f"Prepare:  {t_prepare:.4f}s")
+print(f"Search:   {t_search:.4f}s")
+print(f"Contract: {t_contract:.4f}s")
+print(f"Total:    {t_prepare+t_search+t_contract:.4f}s")
+print(f"Expectation: {0.5 * complex(val).real:.10f}")