IBM added this course when it moved Quantum Learning onto the public platform. It covers how to estimate eigenvalues of large operators by combining quantum sampling with classical diagonalization, including VQE as the baseline and newer sample-based methods (SQD, SKQD).
The overview is aimed at people who already have some quantum computing experience and want to use it on matrix diagonalization. Lessons walk through implementations on real quantum computers and the factors that drive classical versus quantum scaling. IBM Training’s exam issues a Credly badge.
This is the technical companion to Integrating Quantum and HPC, which treats SQD at a high level. For a gentler VQE start, use Variational Algorithm Design.
Who it is for
Researchers and developers chasing near-term chemistry and materials workloads, not beginners.
Topics covered
Start this course
Quantum Diagonalization Algorithms (IBM Learning) is free on IBM Quantum, at advanced level, and runs 10 hours. A certificate is included.
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