add pennylane in bp

Author: refraction-ray

README.md

@@ -109,7 +109,7 @@ We also have [Docker support](/docker).
 
 - Efficiency
 
-  - Time: 10 to 10^6 times acceleration compared to tfq or qiskit
+  - Time: 10 to 10^6+ times acceleration compared to TensorFlow Quantum, Pennylane or Qiskit
 
   - Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)
 

README_cn.md

@@ -105,7 +105,7 @@ pip install tensorcircuit-nightly
 
 - 效率
 
-  - 时间：与 TFQ 或 Qiskit 相比，加速 10 到 10^6 倍
+  - 时间：与 TFQ, Pennylane, 或 Qiskit 相比，加速 10 到 10^6+ 倍
 
   - 空间：600+ qubits 1D VQE 工作流（收敛能量误差：< 1%）
 

examples/bp_benchmark.py

@@ -9,6 +9,7 @@
 import cirq
 import sympy
 import numpy as np
+import pennylane as qml
 import tensorflow_quantum as tfq
 import tensorcircuit as tc
 
@@ -123,6 +124,42 @@ def op_expectation(params, seed, n_qubits, depth):
 )
 
 
+def pennylane_approach(n_qubits=10, depth=10, n_circuits=100):
+
+    dev = qml.device("lightning.qubit", wires=n_qubits)
+    gate_set = [qml.RX, qml.RY, qml.RZ]
+
+    @qml.qnode(dev)
+    def rand_circuit(params, status):
+        for i in range(n_qubits):
+            qml.RY(np.pi / 4, wires=i)
+
+        for j in range(depth):
+            for i in range(n_qubits):
+                gate_set[status[i, j]](params[j, i], wires=i)
+
+            for i in range(n_qubits - 1):
+                qml.CZ(wires=[i, i + 1])
+
+        return qml.expval(qml.Hamiltonian([1.0], [qml.PauliZ(0) @ qml.PauliZ(1)], True))
+
+    gf = qml.grad(rand_circuit, argnum=0)
+    params = np.random.uniform(0, 2 * np.pi, size=[n_circuits, depth, n_qubits])
+    status = np.random.choice(3, size=[n_circuits, depth, n_qubits])
+
+    g_results = []
+
+    for i in range(n_circuits):
+        g_results.append(gf(params[i], status[i]))
+
+    g_results = np.stack(g_results)
+
+    return np.std(g_results[:, 0, 0])
+
+
+benchmark(pennylane_approach)
+
+
 def tc_approach(n_qubits=10, depth=10, n_circuits=100):
 
     seed = tc.array_to_tensor(