Join us as we explore sample-based quantum diagonalization (SQD)—an efficient and scalable method for tackling real-world problems that can be cast as matrix eigenvalue problems. Discover how quantum sampling reduces your problem size and enables classical computing to solve it in a reduced space. Building on concepts from our previous videos, we’ll show why SQD outpaces estimator-based methods in many application areas. Finally, we’ll hint at how SQD pairs with the Krylov method for even more power in quantum computation.
Check out the full course with supporting text and code on IBM Quantum Learning here: https://quantum.cloud.ibm.com/learning/en/courses/quantum-diagonalization-algorithms
For more on SQD, see this tutorial: https://quantum.cloud.ibm.com/docs/en/tutorials/sample-based-quantum-diagonalization
For more on how SQD and the quantum Krylov method work together, see this tutorial: https://quantum.cloud.ibm.com/docs/en/tutorials/sample-based-krylov-quantum-diagonalization
For more quantum computing learning resources visit IBM Quantum Learning: https://quantum.cloud.ibm.com/learning/en
