tried new title description format all lessons

Author: christopherporter1

learning/courses/integrating-quantum-and-high-performance-computing/introduction.ipynb

@@ -5,6 +5,10 @@
    "id": "3d669ed3-1ac7-440c-b744-1155d5a65a6e",
    "metadata": {},
    "source": [
+    "---\n",
+    "title: Introduction\n",
+    "description: This lesson introduces the course in integrating quantum and high-performance computing and includes a course outline. \n",
+    "---\n",
     "## Welcome to the Frontier\n",
     "\n",
     "Throughout history, our scientific understanding of the world has grown rapidly when new tools became available – tools that allowed us to ask new questions, run larger experiments, and investigate new areas of research. Some of those tools include the telescope, the microscope, and the Large Hadron Collider. Each one provided access to new kinds of scientific discoveries. In the 1960s, we also saw the development of high-performance computing (HPC), which became a critical tool for solving complex computational tasks, including many important scientific challenges.\n",

learning/courses/integrating-quantum-and-high-performance-computing/next-steps.ipynb

@@ -5,6 +5,10 @@
    "id": "56f4d5fa-ecb8-4cf3-9ee6-ba3a0e9ba810",
    "metadata": {},
    "source": [
+    "---\n",
+    "title: Next steps\n",
+    "description: This lesson examines the state of quantum computing and how humans are approaching quantum advantage. \n",
+    "---\n",
     "# Chapter 5: Future outlook and direction\n",
     "\n",
     "So far, we have learned about the motivation for using both high-performance computing (HPC) and quantum computing to solve scientific problems. We have defined classical and quantum compute resources, including CPUs, GPUs, and QPUs, and discussed how to scale and manage them using techniques like vertical and horizontal scaling, scheduling, and workload management. Furthermore, we have explored programming models for both QPUs (such as quantum circuits and primitives like Sampler and Estimator) and classical computers, including parallel programming practice with MPI which is a powerful tool of a quantum-classical heterogeneous computing. Finally, we have studied and practiced advanced quantum sampling-based algorithms, like Sample-based Quantum Diagonalization (SQD) and Sample-based Krylov Quantum Diagonalization (SKQD). These algorithms leverage the subspace method to accurately estimate the ground state energy of molecules and materials by preparing and sampling quantum states, which define a subspace for classical diagonalization, a combination of different programming models on a set of heterogeneous resources. With these foundational concepts of quantum and classical supercomputing, we are no longer talking about one replacing the other, but about creating a powerful, integrated system that works in synergy — a combination poised to bring about the dawn of quantum advantage.\n",

learning/courses/integrating-quantum-and-high-performance-computing/programming-models.ipynb

@@ -5,6 +5,10 @@
    "id": "eb6ee18a-db41-4273-bb85-ba1c233c6b64",
    "metadata": {},
    "source": [
+    "---\n",
+    "title: Programming models\n",
+    "description: This lesson gives an overview of programming models for distributing tasks optimally between resources. \n",
+    "---\n",
     "# Programming models\n",
     "\n",
     "Programming models are fundamental specifications that define how software is structured and executed. They provide a framework for developers to express algorithms and organize code, often abstracting away low-level details of the underlying hardware or execution environment. Different models are suited to different types of problems and hardware architectures, offering varying levels of abstraction and control.\n",

learning/courses/integrating-quantum-and-high-performance-computing/sqd-skqd.ipynb

@@ -5,6 +5,10 @@
    "id": "3201f81e-9a9a-4168-b738-58c54a5f5c8b",
    "metadata": {},
    "source": [
+    "---\n",
+    "title: SQD and SKQD\n",
+    "description: This lesson uses an example algorithmic approach (SQD and the closely-related SKQD) to demonstrate how classical and quantum resources can be leveraged together. \n",
+    "---\n",
     "# SQD and SKQD\n",
     "\n",
     "In this chapter, we'll explore how quantum and classical computers work together to solve one of the most important challenges in science: accurately estimating the energy of molecules and materials.\n",