Quantum Neuron

notebooks/textbook/Bells_Inequality.ipynb

@@ -215,9 +215,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "conda_braket",
    "language": "python",
-   "name": "python3"
+   "name": "conda_braket"
   },
   "language_info": {
    "codemirror_mode": {
@@ -229,7 +229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.7.12"
   },
   "vscode": {
    "interpreter": {

notebooks/textbook/Quantum_Approximate_Optimization_Algorithm.ipynb

@@ -2,15 +2,15 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8b423ad7",
+   "id": "455cd77b",
    "metadata": {},
    "source": [
     "# QUANTUM APPROXIMATE OPTIMIZATION ALGORITHM (QAOA)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "12e108d5",
+   "id": "849472aa",
    "metadata": {},
    "source": [
     "In this noteook, we show how to (approximately) solve binary combinatorial optimization problems, using the __Quantum Approximate Optimization Algorithm (QAOA)__.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "bd0a975d",
+   "id": "f59d6171",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "50e4d13a",
+   "id": "75c20f85",
    "metadata": {},
    "outputs": [
     {
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6e26acc1",
+   "id": "d6012510",
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "223d34d0",
+   "id": "1a1a0687",
    "metadata": {},
    "outputs": [
     {
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8cc043c3",
+   "id": "8ea08a49",
    "metadata": {},
    "source": [
     "## Run on a local simulator\n",
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "c700195c",
+   "id": "d0dfe14f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "bc524eef",
+   "id": "bab1d2ca",
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "16df2abe",
+   "id": "5d313165",
    "metadata": {},
    "outputs": [
     {
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "96992efa",
+   "id": "2b6ef1c3",
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64478182",
+   "id": "8ca8961a",
    "metadata": {},
    "source": [
     "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2)."
@@ -238,9 +238,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.5 64-bit ('braket')",
+   "display_name": "conda_braket",
    "language": "python",
-   "name": "python3"
+   "name": "conda_braket"
   },
   "language_info": {
    "codemirror_mode": {
@@ -252,7 +252,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.7.12"
   },
   "vscode": {
    "interpreter": {

notebooks/textbook/Template.ipynb

@@ -107,9 +107,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.5 64-bit ('braket')",
+   "display_name": "conda_braket",
    "language": "python",
-   "name": "python3"
+   "name": "conda_braket"
   },
   "language_info": {
    "codemirror_mode": {
@@ -121,15 +121,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.7.12"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "5904cb9a2089448a2e1aeb5d493d227c9de33e591d7c07e4016fb81e71061a5d"
    }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

src/braket/experimental/algorithms/quantum_approximate_optimization/__init__.py

@@ -15,4 +15,4 @@
     cost_function,
     qaoa,
     run_qaoa_circuit,
-)
+)

src/braket/experimental/algorithms/quantum_neuron/__init__.py

@@ -0,0 +1,18 @@
+# Copyright Amazon.com Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+from braket.experimental.algorithms.quantum_neuron.quantum_neuron import (  # noqa: F401
+    generate_random_numbers,
+    linear_combination,
+    quantum_neuron,
+    activation_function
+)