NVIDIA · elsayedi · Mar 4, 2026 · Mar 5, 2026 · Mar 5, 2026 · Mar 5, 2026
diff --git a/Dockerfile b/Dockerfile
diff --git a/Guide-to-cuda-q-backends.ipynb b/Guide-to-cuda-q-backends.ipynb
diff --git a/ai-for-quantum/.ipynb_checkpoints/README-checkpoint.md b/ai-for-quantum/.ipynb_checkpoints/README-checkpoint.md
@@ -16,11 +16,6 @@ For example, the first notebook guides learners through using a pretrained diffu
 * ***Hands-on experience inserting AI models within quantum workflows:*** Learn how to prepare quantum data for input.
 
 ## Notebooks
-The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).  A Dockerfile and requirements.txt are also included in the main directory of the repository to help get you set up.
+The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).
 
-Otherwise, if you have set up an account in Google CoLab, 
-simply log in to the account, then click on the icons below to run the notebooks on the listed platform.   
-
-| Notebook      | Google Colab |
-| -----------  | ----------- |
-|Lab 1 - Compiling Unitaries with Diffusion Models | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/ai-for-quantum/01_compiling_unitaries_using_diffusion_models.ipynb)|
+Otherwise, explore our [Learning Pathways page](https://nvidia.github.io/cuda-q-academic/learningpath.html) for additional cloud-based options to run these notebooks.
diff --git a/ai-for-quantum/01_compiling_unitaries_diffusion.ipynb b/ai-for-quantum/01_compiling_unitaries_diffusion.ipynb
diff --git a/ai-for-quantum/01_compiling_unitaries_using_diffusion_models.ipynb b/ai-for-quantum/01_compiling_unitaries_using_diffusion_models.ipynb
diff --git a/ai-for-quantum/README.md b/ai-for-quantum/README.md
@@ -16,11 +16,6 @@ For example, the first notebook guides learners through using a pretrained diffu
 * ***Hands-on experience inserting AI models within quantum workflows:*** Learn how to prepare quantum data for input.
 
 ## Notebooks
-The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).  A Dockerfile and requirements.txt are also included in the main directory of the repository to help get you set up.
+The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).
 
-Otherwise, if you have set up an account in Google CoLab, 
-simply log in to the account, then click on the icons below to run the notebooks on the listed platform.   
-
-| Notebook      | Google Colab |
-| -----------  | ----------- |
-|Lab 1 - Compiling Unitaries with Diffusion Models | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/ai-for-quantum/01_compiling_unitaries_using_diffusion_models.ipynb)|
+Otherwise, explore our [Learning Pathways page](https://nvidia.github.io/cuda-q-academic/learningpath.html) for additional cloud-based options to run these notebooks.
diff --git a/chemistry-simulations/README.md b/chemistry-simulations/README.md
@@ -4,17 +4,6 @@ This collection of notebooks explores techniques for calculating molecular groun
 *Pre-requisites:* Learners should have familiarity with Jupyter notebooks and programming in Python and CUDA-Q. Since these notebooks cover chemistry and materials science simulations, domain knowledge is helpful. It is assumed the reader has some familiarity already with quantum computation and is comfortable with braket notation and the concepts of qubits, quantum circuits, measurement, and circuit sampling. The CUDA-Q Academic course entitled "Quick Start to Quantum Computing with CUDA-Q" provide a walkthrough of this prerequisite CUDA-Q knowledge if the reader is new to quantum computing and CUDA-Q or needs refreshing.
 
 ## Notebooks
-The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).  A Dockerfile and requirements.txt are also included in the main directory of the repository to help get you set up.
-
-Otherwise, if you have set up an account in Google CoLab, 
-simply log in to the account, then click on the icons below to run the notebooks on the listed platform.   
-
-| Notebook      | Google Colab |
-| -----------  | ----------- |
-|Lab 1 - Solving the Ground State Problem with VQE and AI (Generative Quantum Eigensolver)  | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/vqe_and_gqe.ipynb)|
-|Lab 2 - ADAPT VQE  | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/adapt_vqe.ipynb)|
-|Lab 3 - Krylov Quantum Subspace Diagonalization  | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/krylov_subspace_diagonalization.ipynb)|
-|Lab 4 - QM/MM: Combining VQE with a Polarizeable Embedding Framework  | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/qmmm.ipynb)|
-|Lab 5 - Canonical, Iterative, and Bayesian Quantum Phase Estimation  | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/qpe.ipynb)|
-
+The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).
 
+Otherwise, explore our [Learning Pathways page](https://nvidia.github.io/cuda-q-academic/learningpath.html) for additional cloud-based options to run these notebooks.
diff --git a/dynamics101/README.md b/dynamics101/README.md
@@ -18,13 +18,6 @@ Designed for advanced users with a solid background in quantum mechanics and fam
 * ***Model Time-Dependent Interactions:*** Learn to implement time-dependent Hamiltonian terms and custom operators to simulate dynamic quantum interactions.
 
 ## Notebooks
-The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).  A Dockerfile and requirements.txt are also included in the main directory of the repository to help get you set up.
-
-Otherwise, if you have set up an account in Google CoLab, 
-simply log in to the account, then click on the icons below to run the notebooks on the listed platform.   
-
-| Notebook      | Google Colab |
-| -----------  | ----------- |
-|Lab 1 - Jaynes-Cummings Hamiltonian  | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/dynamics101/01_Jaynes_Cummings.ipynb)|
-| Lab 2 - Time Dependent Hamiltonians  |[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/dynamics101/02_Time_Dependent.ipynb)  | |||
+The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).
 
+Otherwise, explore our [Learning Pathways page](https://nvidia.github.io/cuda-q-academic/learningpath.html) for additional cloud-based options to run these notebooks.
diff --git a/hybrid-workflows/README.md b/hybrid-workflows/README.md
@@ -3,11 +3,6 @@ Welcome to the hybrid workflows learning path.
 In this path you will learn about workflows that leverage classical AI supercomputing alongside QPUs. Note that most of the other CUDA-Q academic learning pathways focus on hybrid workflows too.  This path will capture lessons that are more general or do not fit within the other more targeted pathways like "chemistry simulations".
 
 ## Notebooks
-The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).  A Dockerfile and requirements.txt are also included in the main directory of the repository to help get you set up.
+The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).
 
-Otherwise, if you have set up an account in Google CoLab, 
-simply log in to the account, then click on the icons below to run the notebooks on the listed platform.   
-
-| Notebook      | Google Colab |
-| -----------  | ----------- |
-|Lab 1 - Quantum Enhanced Memetic-Tabu Search Applied to the LABS Problem | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/hybrid-workflows/01_quantum_enhanced_optimization_LABS.ipynb)
+Otherwise, explore our [Learning Pathways page](https://nvidia.github.io/cuda-q-academic/learningpath.html) for additional cloud-based options to run these notebooks.
diff --git a/images/Adapt-QAOA-flowchart.png b/images/Adapt-QAOA-flowchart.png
diff --git a/images/QAOA-GPT-flowchart.png b/images/QAOA-GPT-flowchart.png
diff --git a/instructions.md b/instructions.md
diff --git a/qaoa-for-max-cut/00_StartHere.ipynb b/qaoa-for-max-cut/00_StartHere.ipynb
@@ -82,7 +82,7 @@
         "id": "48754d6e"
       },
       "source": [
-        "The Jupyter notebooks in this folder are designed to run in an environment with CUDA-Q with Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).  A Dockerfile and requirements.txt are also including in this folder to help get you set up.\n",
+        "The Jupyter notebooks in this folder are designed to run in an environment with CUDA-Q with Python.  For instructions on how to install CUDA-Q on your machine, check out this [guide](https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html#install-cuda-q).\n",
         "\n",
         "For links to run the notebooks in qBraid, CoCalc, or Google CoLab, please see the [READ_ME.md](https://github.com/NVIDIA/cuda-q-academic/blob/main/qaoa-for-max-cut/READ_ME.md) file in this directory."
       ]