update

Author: mhjensen

doc/pub/week10/pdf/week10.pdf

@@ -1095,6 +1095,7 @@
 \begin{itemize}
 \item Continuation of project 1 with  more realistic systems and using adaptive VQE on actual (real) quantum computers
 \item Implementation and studies of the Quantum Approximate Optimization Algorithm (QAOA): Simulation of linear algebra systems on quantum computers, solving for example differential equations
+\item Implementation of the HHL algorithm (needs QPE)
 \item Applications and implementations of quantum machine learning algorithms
 \item Implement quantum neural networks for PINNs
 \item Detailed quantum chemistry applications.
@@ -1114,10 +1115,30 @@
 \end{itemize}
 \end{frame}
 
+\begin{frame}{What is the QAOA algorithm?}
+\begin{block}{QAOA stands for the Quantum Approximate Optimization Algorithm}
+Each part of the name reflects something essential about the method:
+\begin{itemize}
+\item Quantum: it runs on a quantum computer, using qubits and quantum gates.
+\item Approximate: it typically finds near-optimal solutions rather than exact ones.
+\item Optimization: it is designed to solve optimization problems (like MaxCut, scheduling, portfolio selection).
+\item Algorithm: it is a well-defined hybrid quantum–classical procedure.
+\end{itemize}
+\end{block}
 
+QAOA is a variational quantum algorithm that uses a parameterized quantum circuit to search for good solutions to hard optimization problems.
+\end{frame}
 
+\begin{frame}{What is the HHL algorithm?}
 
+The HHL algorithm—named after Aram Harrow, Avinatan Hassidim, and Seth
+Lloyd—is one of the central quantum algorithms for solving linear
+systems of equations.
 
-
+The core idea is to solve a linear system
+\[
+A \mathbf{x} = \mathbf{b}.
+\]
+\end{frame}
 \end{document}