{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a simple quantum circuit using qandle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# add the parent directory to the path so that we can import the module\n",
    "import sys\n",
    "import os\n",
    "\n",
    "current_dir = os.getcwd()\n",
    "sys.path.append(os.path.dirname(os.path.dirname(current_dir)))\n",
    "sys.path.append(os.path.dirname(current_dir))\n",
    "import qandle\n",
    "\n",
    "qandle.__reimport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qandle\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a Quantum Circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = qandle.Circuit(\n",
    "    layers=[\n",
    "        # embedding layer\n",
    "        qandle.AngleEmbedding(qubits=[0, 1], name=\"input0\"),\n",
    "        # trainable layer, with random initialization\n",
    "        qandle.RX(qubit=1),\n",
    "        # trainable layer, with fixed initialization\n",
    "        qandle.RY(qubit=0, theta=torch.tensor(0.2)),\n",
    "        # data reuploading\n",
    "        qandle.RX(qubit=0, name=\"input1\"),\n",
    "        # disable quantum weight remapping\n",
    "        qandle.RY(qubit=1, remapping=None),\n",
    "        qandle.CNOT(control=0, target=1),\n",
    "        qandle.MeasureProbability(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# print the circuit as a torch module\n",
    "circuit\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_state = torch.rand(circuit.num_qubits, dtype=torch.float) # random input state\n",
    "input0 = torch.tensor([0.2, 0.1])\n",
    "input1 = torch.tensor(0.5) \n",
    "\n",
    "circuit(input_state, input0=input0, input1=input1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export it to QASM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qasm3 = qandle.convert_to_qasm(circuit, qasm_version=3)\n",
    "print(qasm3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the circuit\n",
    "Use `circuit.draw()` to get a simple string representation of the circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(circuit.draw())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qc2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}