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Explore sample-based quantum diagonalization (SQD) as an efficient and scalable method for solving real-world problems that can be formulated as matrix eigenvalue problems in this 15-minute video. Learn how quantum sampling techniques reduce problem size and enable classical computing to solve problems in a reduced space. Discover why SQD outperforms estimator-based methods across many application areas, building upon concepts from previous quantum computing videos. Gain insights into how SQD combines with the Krylov method to enhance quantum computation power and efficiency for tackling complex computational challenges.
