From 9e1bc60be72363590f49fbeae24b45e356d08cc8 Mon Sep 17 00:00:00 2001 From: Christian Bruun Madsen Date: Tue, 28 Feb 2023 15:30:36 -0700 Subject: [PATCH] fix: typo in QAOA fix: spelling of alternating --- .../textbook/Quantum_Approximate_Optimization_Algorithm.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/textbook/Quantum_Approximate_Optimization_Algorithm.ipynb b/notebooks/textbook/Quantum_Approximate_Optimization_Algorithm.ipynb index 94c269fa..a4d88430 100644 --- a/notebooks/textbook/Quantum_Approximate_Optimization_Algorithm.ipynb +++ b/notebooks/textbook/Quantum_Approximate_Optimization_Algorithm.ipynb @@ -15,7 +15,7 @@ "source": [ "In this noteook, we show how to (approximately) solve binary combinatorial optimization problems, using the __Quantum Approximate Optimization Algorithm (QAOA)__.\n", "\n", - "The QAOA is a variational quantum algorithm that uses alternativing layers of parameterized quantum gates to solve an optimization problem [1]. The parameters of each gate are tuned to minimize a cost function, similar to how machine learning parameters are tuned in stochastic gradient descent. \n", + "The QAOA is a variational quantum algorithm that uses alternating layers of parameterized quantum gates to solve an optimization problem [1]. The parameters of each gate are tuned to minimize a cost function, similar to how machine learning parameters are tuned in stochastic gradient descent. \n", "\n", "## References \n", "[1] Edward Farhi, Jeffrey Goldstone, and Sam Gutmann, \"A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem,\" (2014), [arXiv:1412.6062](https://arxiv.org/abs/1411.4028)"