Merge branch 'hyperedge-support-16871959254681859317' of github.com:tensorcircuit/tensorcircuit-ng into hyperedge

Author: refraction-ray

examples/hyperedge_demo.py

@@ -0,0 +1,78 @@
+"""
+Demonstration of hyperedge support using cotengra in TensorCircuit.
+"""
+
+import time
+import numpy as np
+import tensornetwork as tn
+import tensorcircuit as tc
+
+
+def hyperedge_demo():
+    print("Demonstrating hyperedge contraction with cotengra...")
+
+    # 1. Single Hyperedge Example
+    # Three tensors A, B, C connected by a single hyperedge (CopyNode)
+    # Result should be sum_i A_i * B_i * C_i
+
+    dim = 2
+    a = tn.Node(np.array([1.0, 2.0]), name="A")
+    b = tn.Node(np.array([1.0, 2.0]), name="B")
+    c = tn.Node(np.array([1.0, 2.0]), name="C")
+    cn = tn.CopyNode(3, dim, name="CN")
+
+    a[0] ^ cn[0]
+    b[0] ^ cn[1]
+    c[0] ^ cn[2]
+
+    nodes = [a, b, c, cn]
+
+    # Set contractor to cotengra
+    tc.set_contractor("cotengra")
+
+    res = tc.contractor(nodes)
+    print("Single Hyperedge Result:", res.tensor)
+    expected = 1 * 1 * 1 + 2 * 2 * 2
+    print(f"Expected: {expected}")
+    assert np.allclose(res.tensor, expected)
+
+    # 2. Large Scale Hyperedge Example
+    # Demonstrate memory and time efficiency with a large number of legs
+    print("\nDemonstrating large scale hyperedge (20 legs)...")
+    num_legs = 20
+    dim = 2
+
+    # Create 20 random tensors connected to a single CopyNode
+    input_tensors = [
+        tn.Node(np.random.rand(dim), name=f"T{i}") for i in range(num_legs)
+    ]
+    cn_large = tn.CopyNode(num_legs, dim, name="CN_Large")
+
+    for i, t in enumerate(input_tensors):
+        t[0] ^ cn_large[i]
+
+    large_nodes = input_tensors + [cn_large]
+
+    start_time = time.time()
+    res_large = tc.contractor(large_nodes)
+    end_time = time.time()
+
+    print(f"Contracted {num_legs} legs in {end_time - start_time:.4f} seconds.")
+    print("Large Hyperedge Result shape:", res_large.tensor.shape)
+
+    # Verification: Explicitly calculate the sum
+    # result = sum_k (prod_i T_i[k])
+
+    # Transpose input tensors to shape (num_legs, dim)
+    tensor_matrix = np.stack([t.tensor for t in input_tensors])
+    # Product along the tensor axis (0) for each dimension index
+    prod_along_legs = np.prod(tensor_matrix, axis=0)
+    expected_sum = np.sum(prod_along_legs)
+
+    print(f"Computed: {res_large.tensor}")
+    print(f"Expected: {expected_sum}")
+    assert np.allclose(res_large.tensor, expected_sum)
+
+
+if __name__ == "__main__":
+    hyperedge_demo()