Self-paced bootcamp in seven parts, September 14–22, 2026
SC Quantathon v3 Bootcamp
About
The bootcamp was the run-up to SC Quantathon v3, with one day of material released on each of seven weekdays from Monday, September 14 to Tuesday, September 22. It is self-paced: each day has written notes and a notebook you can run (Day 3 is a recorded demo), so you can work through it whenever suits you and come back to anything you want to see again. Nothing is graded.
The seven days build from the basics up. The first week gets you set up with Python and your first circuit, covers the math behind a qubit, demos the qBraid platform, works through gates, measurement, and entanglement, and ends by running circuits on real hardware. The second week covers variational algorithms and closes with a full day on quantum machine learning.
It is built for people who have never written a quantum circuit before, and it is open to everyone. You do not need to have registered for the hackathon, or to have attended it, to work through it. Every day below has its files posted, and the same files are mirrored in the SC Quantathon v3 GitHub repository (opens in a new tab).
Daily Content
Open a day for its notes and notebook.
Day 1Setup, Python Refresher, and Your First CircuitMon, Sep 14
Setting up Qiskit or qBraid Lab and saving your IBM Quantum credentials, a Python and NumPy refresher, bits and gates as tables, what a qubit changes, and a one-qubit random number generator run on a simulator and then on a real processor. Read the notes, then work through the notebook; the solutions notebook has every exercise filled in with its outputs.
Notes: Day 1 (opens in a new tab)Notebook: Day 1Solutions: Day 1GitHub: Day 1 (opens in a new tab)
Day 2The Math Behind a QubitTue, Sep 15
Complex numbers, a qubit as a vector and the Born rule, Dirac notation, global and relative phase, the Bloch sphere, gates as unitary matrices, interference, and measuring |+⟩ and |−⟩ on a real processor.
Notes: Day 2 (opens in a new tab)Notebook: Day 2Solutions: Day 2GitHub: Day 2 (opens in a new tab)
Day 3qBraid Platform Demo: Lab, Credits, and Submitting to a QPUWed, Sep 16
Recorded on September 16. Alex Van Bussum of qBraid walks through the qBraid platform: launching Lab, how the hackathon credits work, and submitting a circuit from Lab to a QPU. Create a free account at account.qbraid.com (opens in a new tab), then launch Lab from the account dashboard before you start. During the hackathon, accepted participants were added to the SC Quantathon v3 organization on qBraid, which is where the credits lived.
Video (opens in a new tab)qBraid account (opens in a new tab)qBraid docs (opens in a new tab)GitHub: Day 3 (opens in a new tab)
Day 4Gates, Measurement, and EntanglementThu, Sep 17
Pauli gates as half-turns, rotations, gate order, measurement and shots, expectation values with the Estimator, tensor products and bit ordering, product versus entangled states, CNOT, CZ, and SWAP, the Bell state and its correlations in three bases, GHZ states and the cost of simulation, and the Bell state on a real processor.
Notes: Day 4 (opens in a new tab)Notebook: Day 4Solutions: Day 4GitHub: Day 4 (opens in a new tab)
Day 5Real Hardware: Transpilation, Noise, and Error MitigationFri, Sep 18
A snapshot of a real processor that runs offline, coupling maps and calibration data, transpilation to native gates, decoherence, gate error, and readout error, the noisy simulator against a real run, readout error mitigation, zero-noise extrapolation, and the same circuit on the live machine.
Notes: Day 5 (opens in a new tab)Notebook: Day 5Solutions: Day 5GitHub: Day 5 (opens in a new tab)
Day 6Variational Algorithms: VQE and QAOAMon, Sep 21
Parameterized circuits, Hamiltonians and the variational principle, VQE on the hydrogen molecule, the parameter-shift rule and three optimizers, VQE under shot and gate noise with readout mitigation, ansatz design and the dissociation curve, QAOA for Max-Cut, and measuring an energy on the live machine.
Notes: Day 6 (opens in a new tab)Notebook: Day 6Solutions: Day 6GitHub: Day 6 (opens in a new tab)
Day 7Quantum Machine LearningTue, Sep 22
Encoding classical data into qubits, quantum kernels with a support vector machine, a variational quantum classifier, gradients and training, barren plateaus, what transfers to the hackathon, and computing a kernel entry on the live machine.
Notes: Day 7 (opens in a new tab)Notebook: Day 7Solutions: Day 7GitHub: Day 7 (opens in a new tab)