Add a notebook for Quantum Neuron

examples/pennylane/7_Quantum_neuron/inputs.txt

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+0000
+0001
+0010
+0011
+0100
+0101
+0110
+0111
+1000
+1001
+1010
+1011
+1100
+1101
+1110
+1111

examples/pennylane/7_Quantum_neuron/qn/quantum_neuron_script.py

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+import json
+import os
+import time
+import sys
+import ast
+
+import numpy as np
+import matplotlib.pyplot as plt
+from typing import List
+from braket.aws import AwsQuantumJob, AwsSession
+from braket.jobs.image_uris import Framework, retrieve_image
+from braket.jobs import load_job_checkpoint, save_job_checkpoint, save_job_result
+from braket.tracking import Tracker
+
+import pennylane as qml
+
+def init_pl_device(device_arn, n_nodes, shots, max_parallel):
+    return qml.device(
+        "braket.aws.qubit",
+        device_arn=device_arn,
+        wires=n_nodes,
+        shots=shots,
+        # Set s3_destination_folder=None to output task results to a default folder
+        s3_destination_folder=None,
+        parallel=True,
+        max_parallel=max_parallel,
+        # poll_timeout_seconds=30,
+    )
+
+def af(inputs:str, weights, bias, ancilla, output, n_nodes):  # af: activation function
+    for qubit in range(len(inputs)):
+        if(inputs[qubit]=='1'):
+            qml.PauliX(qubit)
+
+    for qubit in range(len(inputs)):
+        qml.CRY(phi=2*weights[qubit], wires=[qubit, ancilla])
+
+    qml.RY(2*bias, wires=ancilla)
+
+    qml.CY(wires=[ancilla, output])
+    qml.RZ(phi=-np.pi/2, wires=ancilla)
+
+    for qubit in range(len(inputs)):
+        qml.CRY(phi=-2*weights[qubit], wires=[qubit, ancilla])  # note '-(minus)'
+
+    qml.RY(-2*bias, wires=ancilla)  # note '-(minus)'
+
+    return [qml.sample(qml.PauliZ(i)) for i in range(n_nodes)]
+
+def quantum_neuron(inputs:str, weights, bias, n_nodes, dev):
+    ancilla = len(weights) # ID of an ancilla qubit
+    output = len(weights) + 1   # ID of an output qubit
+
+    theta = np.inner(np.array(list(inputs), dtype=int), np.array(weights)) + bias   # linear comination with numpy
+    theta = theta.item()   # Convert numpy array to native python float-type
+
+    af_circuit = qml.QNode(af, dev)
+    sample = af_circuit(inputs, weights, bias, ancilla, output, n_nodes)
+    sample = sample.T
+    sample = (1 - sample.numpy()) / 2
+
+    adopted_sample = sample[sample[:,ancilla] == 0]
+
+    count_0 = len(adopted_sample[adopted_sample[:,output] == 0])
+    count_1 = len(adopted_sample[adopted_sample[:,output] == 1])
+
+    p_0 = count_0 / (count_0 + count_1)
+
+    q_theta = np.arccos(np.sqrt(p_0))
+
+    return theta, q_theta
+
+def main():
+    t = Tracker().start()
+
+    input_dir = os.environ["AMZN_BRAKET_INPUT_DIR"]
+    output_dir = os.environ["AMZN_BRAKET_JOB_RESULTS_DIR"]
+    job_name = os.environ["AMZN_BRAKET_JOB_NAME"]  # noqa
+    checkpoint_dir = os.environ["AMZN_BRAKET_CHECKPOINT_DIR"]  # noqa
+    hp_file = os.environ["AMZN_BRAKET_HP_FILE"]
+    device_arn = os.environ["AMZN_BRAKET_DEVICE_ARN"]
+
+    # Read the hyperparameters
+    with open(hp_file, "r") as f:
+        hyperparams = json.load(f)
+
+    n_inputs = int(hyperparams["n_inputs"])
+    weights = ast.literal_eval(hyperparams["weights"])
+    bias = float(hyperparams["bias"])
+    shots = int(hyperparams["shots"])
+    interface = hyperparams["interface"]
+    max_parallel = int(hyperparams["max_parallel"])
+
+    if "copy_checkpoints_from_job" in hyperparams:
+        copy_checkpoints_from_job = hyperparams["copy_checkpoints_from_job"].split("/", 2)[-1]
+    else:
+        copy_checkpoints_from_job = None
+
+    # Read input strings from input file
+    with open(f"{input_dir}/input/inputs.txt") as f:
+        inputs = [s.strip() for s in f.readlines()]
+
+    # Prepare quantum neuron circuit
+    n_nodes = n_inputs+2 # +2: ancilla and output qubit
+
+    # Run quantum neuron circuit
+    dev = init_pl_device(device_arn, n_nodes, shots, max_parallel)
+    theta_list = []
+    q_theta_list = []
+
+    for i in range(2**n_inputs):
+        theta, q_theta = quantum_neuron(inputs[i], weights, bias, n_nodes, dev)
+
+        theta_list.append(theta)
+        q_theta_list.append(q_theta)
+
+    save_job_result({"theta_list": theta_list, "q_theta_list": q_theta_list, "task summary": t.quantum_tasks_statistics(), "estimated cost": t.qpu_tasks_cost() + t.simulator_tasks_cost()})
+
+if __name__ == "__main__":
+    main()