构建基于oneapi的mpi4py，quimb支持mpi多机并行，缩短路径找寻时间

tests/quimb_mpi2.py

@@ -0,0 +1,81 @@
+import time
+import numpy as np
+import quimb.tensor as qtn
+import cotengra as ctg
+from mpi4py import MPI
+
+comm = MPI.COMM_WORLD
+rank = comm.Get_rank()
+size = comm.Get_size()
+
+def run_mpi(n_qubits, depth):
+    if rank == 0:
+        print(f"MPI size: {size} ranks")
+        print(f"Circuit: {n_qubits} qubits, depth {depth}")
+
+    # 1. 所有 rank 独立构建电路（避免广播大对象）
+    circ = qtn.Circuit(n_qubits, dtype=np.complex128)
+    for _ in range(depth):
+        for i in range(n_qubits):
+            circ.apply_gate('H', i)
+        for i in range(0, n_qubits - 1, 2):
+            circ.apply_gate('CZ', i, i + 1)
+    psi = circ.psi
+    net = psi.conj() & psi
+
+    # 2. 所有 rank 并行搜索路径，rank 0 选全局最优
+    t0 = time.perf_counter()
+    repeats_per_rank = max(1, 128 // size)
+    opt = ctg.HyperOptimizer(
+        methods=['kahypar'],
+        max_repeats=repeats_per_rank,
+        minimize='flops',
+        parallel=max(1, 96 // size),
+    )
+    local_tree = net.contraction_tree(optimize=opt)
+
+    all_trees = comm.gather(local_tree, root=0)
+
+    if rank == 0:
+        tree = min(all_trees, key=lambda t: t.contraction_cost())
+        t1 = time.perf_counter()
+        print(f"[rank 0] Path search: {t1 - t0:.4f} s")
+    else:
+        tree = None
+
+    tree = comm.bcast(tree, root=0)
+
+    # 3. rank 0 切片，broadcast sliced_tree
+    if rank == 0:
+        sliced_tree = tree.slice(target_size=2**27)
+    else:
+        sliced_tree = None
+    sliced_tree = comm.bcast(sliced_tree, root=0)
+    n_slices = sliced_tree.nslices
+
+    if rank == 0:
+        print(f"Total slices: {n_slices}, each rank handles ~{n_slices // size}")
+
+    arrays = [t.data for t in net.tensors]
+
+    # 每个 rank 处理自己负责的切片
+    t2 = time.perf_counter()
+    local_result = 0.0 + 0.0j
+    for i in range(rank, n_slices, size):
+        local_result += sliced_tree.contract_slice(arrays, i, backend='numpy')
+    t3 = time.perf_counter()
+
+    # 4. reduce 汇总到 rank 0
+    total = comm.reduce(local_result, op=MPI.SUM, root=0)
+
+    if rank == 0:
+        print(f"[rank 0] Contract: {t3 - t2:.4f} s")
+        print(f"Result: {total:.10f}")
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--n_qubits", type=int, default=50)
+    parser.add_argument("--depth", type=int, default=20)
+    args = parser.parse_args()
+    run_mpi(args.n_qubits, args.depth)