Notebook Catalog
Florida Qiskit Fallfest Hackathon, part of Qiskit Fall Fest 2026.
Every notebook below is free, official (IBM Quantum Learning or Qiskit documentation), and maintained against current Qiskit. Each one is rated by how much code it asks you to touch, so you can start with a page that fits you.
Domain/Industry Expert: sections 1, 2, and 4 were written for you. Section 2 is quantum work pointed at industries, which is a friendly place to spark a Use-Case Canvas. Bring your curiosity. We’ll help with the qubits.
How to read the ratings
| Means | |
|---|---|
| 🟢 No code | Interactive or visual. Nothing to install, nothing to type. |
| 🟡 Click Run | A notebook you execute cell by cell. You read the explanations, press Run, look at the output. No editing required. |
| 🟠 Read code | Worth reading even if you don't write Python. Follow the narrative, skip the implementation. |
| 🔴 Write code | You'll be editing and debugging. Builder/Developer Expert. |
1. Start here — zero code required
🟢 IBM Quantum Composer (opens in a new tab) Drag-and-drop circuit builder. Place gates on a circuit and watch the state visualizations respond in real time. A hardware run is optional and uses Open Plan time, so the simulator is a good first click. A browser and an IBM Quantum account are enough. About twenty minutes here makes later conversations easier. Both pathways are welcome.
🟢 Superposition (opens in a new tab) A short teaching module on the property that makes quantum different. It explains superposition as a weighted combination, which is a clearer picture than "both at once."
🟢 Quantum teleportation (opens in a new tab) Despite the name, no matter is transported anywhere. This is about moving quantum information, and it's the most memorable demonstration of why entanglement is a resource rather than a curiosity.
🟢 Quantum key distribution (opens in a new tab) Using quantum statistics to detect an eavesdropper. Directly relevant to anyone thinking about security and policy — and one of the few areas with genuine near-term deployment.
🟢 Uncertainty (opens in a new tab) Where measurement uncertainty comes from. Useful background for understanding why quantum results are probability distributions rather than answers.
Full module library: https://quantum.cloud.ibm.com/learning/modules (opens in a new tab)
2. Domain and industry notebooks
This is the section to read if you're bringing industry expertise. These are end-to-end examples of quantum methods pointed at recognisable commercial problems. Several are built by quantum software companies rather than IBM, which makes them useful in a second way: you can see how the industry frames its own value proposition, and judge it.
Read them for how the problem is set up — what got simplified, what constraints were kept, what the comparison baseline was. That critical reading is exactly the skill your team will need from you.
Finance
🟠 Dynamic portfolio optimization (opens in a new tab) (Global Data Quantum) Multi-period portfolio construction as a quantum optimization problem. The most directly recognisable finance example in the catalog. Read the problem formulation section and ask yourself which real-world constraints are missing.
🟠 Solve the Market Split problem (opens in a new tab) (Kipu Quantum) A classic hard combinatorial problem with direct commercial analogues in allocation and division.
Energy and utilities
🟠 Hybrid quantum-enhanced ensemble classification — grid stability (opens in a new tab) A machine learning workflow applied to power grid stability. Good example of the hybrid pattern: classical does most of the work, quantum handles one piece.
Chemistry, materials, and pharma
🟠 Dissociation PES curves with HiVQE (opens in a new tab) (Qunova) Potential energy surfaces — the computation underneath reaction and binding prediction. This is the application category where the theoretical match between the problem and a quantum machine is strongest. That is not a claim that a weekend project showed a quantum method beating the best classical method. The benchmarking page defines that comparison.
🟠 Sample-based quantum diagonalization of a chemistry Hamiltonian (opens in a new tab) One of the more current approaches to chemistry on near-term hardware.
🟠 Implicit solvent calculations (opens in a new tab) Modelling molecules in solution rather than in a vacuum — the step that makes chemistry simulation relevant to drug discovery rather than a physics exercise.
Engineering and simulation
🟠 Model a flowing non-viscous fluid — QUICK-PDE (opens in a new tab) (ColibriTD) Partial differential equations, which underlie fluid dynamics, structural analysis, heat transfer, and a large fraction of engineering simulation.
🟠 Simulate neutron scattering in quantum materials (opens in a new tab) Materials characterisation. Relevant to anyone in advanced manufacturing or materials R&D.
General optimization
🟠 Higher-order binary optimization (opens in a new tab) (Q-CTRL) A general-purpose optimization solver. Useful for seeing what shape a problem has to be in before it can be handed to one of these.
3. Core algorithms
Builder/Developer Expert material, but the narrative sections are readable by anyone.
🟡 CHSH inequality (opens in a new tab) IBM's designated first tutorial, and a friendly choice: you can run an experiment on a real quantum computer that shows a result classical physics does not predict. It takes about ten minutes in the browser. If you run one notebook during the event, this one is a welcoming place to start. A simulator run is fine if you want to save QPU time.
🟡 Hello world / your first circuit on hardware (opens in a new tab) The complete end-to-end loop: build, transpile, submit, retrieve, interpret. Every project you write will follow this skeleton.
🟠 Grover's algorithm (opens in a new tab) Quantum search. The famous quadratic speedup. Read the introduction for a clear statement of what the speedup is and — importantly — what it isn't.
🟠 Shor's algorithm (opens in a new tab) Integer factoring. Many people have heard of it because of cryptography. The resource requirements section is a careful picture of what the algorithm needs. It is a good read before a conversation about quantum computing and encryption.
🔴 Quantum approximate optimization algorithm (QAOA) (opens in a new tab) The workhorse for optimization projects. If your team's use case is Shape 1, start here.
🔴 Advanced techniques for QAOA (opens in a new tab) · 🔴 Warm-start QAOA (opens in a new tab) Follow-ups for teams going deep on optimization.
🔴 Ground-state energy estimation with VQE (opens in a new tab) The variational pattern. The foundation for most chemistry and materials work.
🔴 Quantum kernel training (opens in a new tab) · 🔴 Projected quantum kernels (opens in a new tab) Quantum machine learning. Start here if your use case is Shape 3.
4. Reading a notebook when you can't read the code
A genuine skill, and one you can pick up in the next ten minutes. Notebooks alternate between prose cells and code cells. The prose is the paper; the code is the appendix.
The method:
- Read the title and opening section. What problem, why it matters. Often this is all you need.
- Scroll straight to the plots and tables at the bottom. The results. What did it produce and what's on the axes?
- Now go back and read only the prose cells. Skip every grey code block entirely. Most well-written notebooks remain fully coherent this way — that's what the prose is for.
- Look for the numbers that matter to you: How many qubits? How many shots? What was it compared against? How long did it take?
- Find the limitations paragraph. Almost every honest notebook has one, usually near the end. This is frequently the most valuable content in the document.
The four questions to ask of any notebook:
- How big was the problem, really? Look for qubit counts and instance sizes. Compare mentally to what a real instance in your field would be. The gap is the story.
- What was it compared to? If there's no classical baseline, no claim about advantage can be supported, full stop.
- What was simplified away? Every notebook simplifies. Which of those simplifications would your industry find unacceptable?
- What would it take to be useful? Sometimes stated. Often it's the thing you can contribute by working it out.
If you can answer those four questions, you understand the notebook well, whether or not you ran the cells. That reading is a real Domain/Industry Expert skill.
5. How to run these
Ordered from the smallest setup to a local install. Any of them is a fine start.
| Method | Setup | Best for |
|---|---|---|
| IBM Quantum Learning (opens in a new tab) in-browser | None | Course lessons. Already connected to your account. |
| Google Colab (opens in a new tab) | One pip line |
Everything else, if you don't want a local install |
| Local Jupyter | See Handbook §5.2 | Project work, offline work, persistent environments |
Colab recipe. Download the notebook (.ipynb) from the tutorial page, upload it to Colab, and add a new first cell:
!pip install qiskit qiskit-ibm-runtime matplotlib pylatexenc
Then Run All. Two caveats: Colab sessions time out and lose state, so save your work to GitHub; and never hardcode your IBM API key in a Colab notebook.
Circuit drawings failing? You're missing pylatexenc. Install it, restart the kernel.
A tutorial from somewhere else does not run? Check the date. Anything with channel="ibm_quantum", IBMQ.load_account(), execute(), or qiskit.Aer predates the current API. See Handbook §4.1. You can keep learning from the notebooks in this catalog while you sort that out. Ask in Discord and include the error text, not an API key.
6. Picking by project type
| Your project is about... | Read these, in order |
|---|---|
| Scheduling, routing, allocation | Higher-order binary optimization → QAOA → Market Split → Advanced QAOA |
| Finance and portfolios | Dynamic portfolio optimization → QAOA → Market Split |
| Chemistry, drugs, materials | Dissociation PES curves → Sample-based diagonalization → Spin-chain VQE → Implicit solvent |
| Classification and prediction | Grid stability classification → Quantum kernel training → Projected quantum kernels |
| Engineering simulation | QUICK-PDE fluid → Neutron scattering |
| Security, crypto, policy | Quantum key distribution → Shor's algorithm → CHSH inequality |
| "I want to understand what this is" | Composer → Superposition → CHSH inequality → Teleportation |
| "I want a hardware reality check" | Probabilistic error amplification (opens in a new tab) → Repetition codes (opens in a new tab) → Real-time benchmarking (opens in a new tab) |
7. Complete official sources
- Tutorials: https://quantum.cloud.ibm.com/docs/en/tutorials (opens in a new tab)
- Courses: https://quantum.cloud.ibm.com/learning/en/courses (opens in a new tab)
- Teaching modules: https://quantum.cloud.ibm.com/learning/modules (opens in a new tab)
- Guides and how-tos: https://quantum.cloud.ibm.com/docs/guides (opens in a new tab)
- API reference: https://quantum.cloud.ibm.com/docs/api (opens in a new tab)
- Qiskit YouTube: https://www.youtube.com/qiskit (opens in a new tab)
Event-specific starter notebooks beyond this catalog are Coming soon. They are scheduled with the October 1, 2026 challenge release.
Hackathon projects are submitted in the private repository https://github.com/robertloredo/FAU-Qiskit-Fallfest-2026 (opens in a new tab). Ask to join it. Copy submissions/_TEMPLATE there after you can open the repository.
Found something excellent that isn't listed, or a link that's broken? Tell us in Discord. Curating this catalog is welcome. Team projects go in the private repository above.