fine tune

Classiq · Feb 16, 2025 · 5f489f4 · 5f489f4
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diff --git a/tutorials/Classiq_tutorial/Execution_tutorial.ipynb b/tutorials/Classiq_tutorial/Execution_tutorial.ipynb
@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# [WIP] Execution Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A typical workflow with classiq ends with the execution of a quantum program on a chosen simulator or hardware (and post-processing the results).\n",
+    "\n",
+    "\n",
+    "Another typical workflow, called _hybrid execution_ is based not on the execution of a single quantum program, but on iterative executions of quantum progarams that are tuned between iterations according to some classical logic (see for example the [VQE algorithm](https://en.wikipedia.org/wiki/Variational_quantum_eigensolver)).\n",
+    "\n",
+    "In this tutorial, we will cover the required knowledge for both usecases:\n",
+    "- Specifying the quantum backend (hardware or simulator) on which to run, and the `n_shots` number of times to run.\n",
+    "- use `ExecutionSession` as a context manager for the execution:\n",
+    "  - Choosing between `sample` (measurement aling the z-axis) and `estimate` as the execution primitive.\n",
+    "  - Creating parametric circuits and executing them for different parameter-values.\n",
+    "  - Using batch-execution to obtain multiple results in  a single command.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specifying Backend and Number of Shots\n",
+    "\n",
+    "Let's look into the following quantum model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Opening: https://platform.classiq.io/circuit/2t8Hqj0F0Ixb67vJRXiGzQLjy0G?version=0.68.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from classiq import *\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QBit]):\n",
+    "    allocate(1, x)\n",
+    "    RX(3.14, x)\n",
+    "\n",
+    "\n",
+    "qmod = create_model(main)\n",
+    "qprog = synthesize(qmod)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[***] show how to specify BE and n_shots, both in IDE and  in code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Execution Session as a Context Manager\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[***] add subsections according to details in the first section.\n",
+    "use Nadav's notebook and the user guide as resources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [Internal]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@qfunc\n",
+    "def main(x: Output[QBit], t: CReal, w: CReal):\n",
+    "    allocate(1, x)\n",
+    "    RX(t, x)\n",
+    "    RZ(w, x)\n",
+    "\n",
+    "\n",
+    "qmod = create_model(main)\n",
+    "qprog = synthesize(qmod)\n",
+    "# execution_session = ExecutionSession(qprog)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "external-user-venv-PaJZMdG0-py3.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
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+   "nbconvert_exporter": "python",
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+}
diff --git a/tutorials/Classiq_tutorial/Qmod_tutorial_part1.ipynb b/tutorials/Classiq_tutorial/Qmod_tutorial_part1.ipynb
@@ -185,7 +185,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exercise #2 - Repeat\n",
+    "### Exercise #2 - The Repeat Statement\n",
     "\n",
     "Use Qmod's [`repeat`](https://docs.classiq.io/latest/qmod-reference/language-reference/statements/classical-control-flow/#classical-repeat) statement to create your own Hadamard Transform - a function that takes a `QArray` of an unspecified size and applies `H` to each of its qubits.\n",
     "\n",

diff --git a/tutorials/Classiq_tutorial/classiq_overview_tutorial.ipynb b/tutorials/Classiq_tutorial/classiq_overview_tutorial.ipynb
@@ -8,7 +8,7 @@
     "In this notebook we introduce a typical workflow with Classiq:\n",
     "- **Designing a quantum algorithm** using the Qmod language and it's accompanied function library.\n",
     "- **Synthesizing the algorithm** into a concrete circuit implementation.\n",
-    "- **Executing the algorithm** on a chosen simulator or quantum harware.\n",
+    "- **Executing the algorithm** on a chosen simulator or quantum hardware.\n",
     "- **Post-processing** the results.\n",
     "\n",
     "Later tutorials dive into each of the above stages, providing hands-on interactive guides that go from the very basics to advanced usage. \n",

diff --git a/tutorials/Classiq_tutorial/synthesis_tutorial.ipynb b/tutorials/Classiq_tutorial/synthesis_tutorial.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Synthesis\n",
+    "# Synthesis Tutorial\n",
     "\n",
     "Classiq synthesis engine takes a high-level program written in Classiq's Qmod language, and compiles it into an executable gate-based circuit.\\\n",
     "In most cases, there is not a single implementation that is superior in all manners - there might be one implementation that requires the lowest number of qubits, another that minimizes the gate-count etc. \n",