Qiskit Fall Fest 2026

Guest seminar (speaker TBD) to open the two weeks with the foundations: what a qubit is, superposition, measurement, the Bloch sphere, gates as rotations, and entanglement through the Bell pair and GHZ states. From there we move to Qiskit: the four steps every Qiskit program follows, and how the same circuit runs on a simulator and on an IBM quantum processor. It closes with how the hardware has changed since 2016.

Seminar by the organizers: how IBM’s superconducting processors work, what T1, T2, and readout error mean, and why circuits are transpiled before they run. The math for the day is multi-qubit states as tensor products and gates as unitary matrices. Then the first steps toward error correction: suppressing and mitigating errors so a noisy processor’s answers land closer to the ideal ones. You run each circuit on a simulator and on an IBM processor and compare the two.

Guest seminar (speaker TBD) on quantum algorithms. First the textbook ones: Grover’s search, which finds a marked item in an unsorted list with quadratically fewer queries, and the quantum Fourier transform, the building block behind phase estimation and Shor’s algorithm. Then the variational ones running on today’s processors: the Max-Cut problem, how a graph becomes an Ising Hamiltonian, what the QAOA circuit is doing, and VQE for finding the ground-state energy of a small molecule. Hybrid quantum-classical methods suit noisy hardware, and the lab shows how to read a good answer out of noisy samples.

Seminar (speaker TBD) on quantum cryptography: why measuring a quantum state disturbs it, the no-cloning theorem, and BB84, the protocol that uses those two facts to share a secret key and catch an eavesdropper. We run BB84 as circuits, add an eavesdropper, and watch the error rate give her away. The seminar closes on why Shor’s algorithm threatens today’s public-key encryption and what post-quantum cryptography is doing about it.

Seminar by the organizers on high-performance computing for quantum: simulating circuits at scale with tensor networks and GPUs, the quantum-centric supercomputing model in which a classical cluster and a quantum processor share the work, and running large simulations on Clemson’s Palmetto 2 cluster. Then quantum machine learning: encoding data into qubits, quantum kernels and variational classifiers.

Seminar (speaker TBD) to close the two weeks with quantum error correction: why a qubit can’t be copied but its information can be spread across many, the bit-flip and repetition codes, and syndrome measurement with mid-circuit measurement and feed-forward. Quantum teleportation uses the same trick, and it is how today’s chips build long-range entanglement. We end with the surface code and what a fault-tolerant processor needs.