From 88ddc411df2fbdb33fb22ab070b7bc1dd82ab35b Mon Sep 17 00:00:00 2001
From: a-matsuo <47442626+a-matsuo@users.noreply.github.com>
Date: Fri, 15 Mar 2024 15:58:45 +0900
Subject: [PATCH] Update docs/notebooks/qaoa-transpiler.ipynb

Co-authored-by: Daniel J. Egger <38065505+eggerdj@users.noreply.github.com>
---
 docs/notebooks/qaoa-transpiler.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/qaoa-transpiler.ipynb b/docs/notebooks/qaoa-transpiler.ipynb
index ce2f55a..d69db07 100644
--- a/docs/notebooks/qaoa-transpiler.ipynb
+++ b/docs/notebooks/qaoa-transpiler.ipynb
@@ -14,7 +14,7 @@
     "In this comprehensive guide, we will walk you through the **Quantum Approximate Optimization Algorithm (QAOA)** workflow using **[Qiskit Patterns](https://www.ibm.com/quantum/blog/qiskit-patterns).**\n",
     "We'll demonstrate the application of these patterns specifically within the domain of **combinatorial optimization** and showcase how the QAOA, a hybrid quantum-classical iterative method, can be employed effectively.\n",
     "\n",
-    "- **Step 1**: Formulating a combinatorial in terms of finding the ground state of a Hamiltonian. This reformulated problem can be understood by a quantum computer.\n",
+    "- **Step 1**: Formulating a combinatorial optimization problem in terms of finding the ground state of a Hamiltonian. This reformulated problem can be understood by a quantum computer.\n",
     "- **Step 2**: Preparation of the necessary quantum circuits optimized for execution on quantum hardware.\n",
     "- **Step 3**: Iteratively utilizing Qiskit's `Sampler` primitive to draw samples from the prepared quantum circuits in Step 2. These samples will inform the loss function of our algorithm routine.\n",
     "- **Step 4**: Converting the samples from Step 3 into a solution for our combinatorial optimization problem.\n",