1 course · 8 tutorials
D-Wave
D-Wave's free introductory course demystifying quantum computing for general audiences. Covers how quantum computers work, annealing vs gate-model systems, and real-world optimization use cases. No background required.
Cut noise on NISQ devices with PennyLane: implement zero-noise extrapolation and probabilistic error cancellation, with a working example that beats the unmitigated baseline.
Learn to simulate realistic quantum noise in PyQuil using Kraus operators, depolarizing channels, and T1/T2 decoherence models. Compare ideal and noisy Bell state results on the QVM.
Build the Quantum Approximate Optimization Algorithm from scratch in Qiskit to solve MaxCut on small graphs. Understand the circuit structure, cost function, and how to tune the depth parameter p.
Use Qiskit Runtime's built-in error mitigation options: resilience levels, zero-noise extrapolation, and Probabilistic Error Cancellation in the Estimator primitive.
A practical comparison of the three main quantum error mitigation strategies (zero-noise extrapolation, probabilistic error cancellation, and Clifford data regression) with working Mitiq code and guidance on when to use each.
Use Mitiq's zero-noise extrapolation to reduce the impact of hardware noise on expectation values without full quantum error correction.
How VQE works: the variational principle, ansatz design, classical optimizer loop, and a complete Qiskit implementation for finding the ground state of a simple Hamiltonian.
A clear comparison of quantum annealing and gate-based quantum computing: how they work, what problems they solve best, and when to choose each approach.