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Learn sample-based quantum diagonalization (SQD), a cutting-edge technique for estimating eigenvalues and eigenvectors of quantum Hamiltonians in this 13-minute tutorial video. Discover how SQD combines quantum sampling with classical post-processing to approximate ground state energies, offering a scalable approach that surpasses exact classical methods and traditional variational algorithms. Explore the significance of this technique for chemistry and physics applications, including molecular stability analysis and material property calculations. Watch Bryce Fuller demonstrate practical implementation of SQD in Qiskit to approximate the ground state energy of nitrogen molecules on real quantum hardware. Access comprehensive resources including Qiskit documentation, the SQD addon code repository, complete chemistry tutorials, function templates for chemistry simulation workflows, and a dedicated learning course on quantum diagonalization algorithms. Delve into supporting research papers covering chemistry applications beyond exact diagonalization scales, quantum-centric Krylov diagonalization algorithms, and large many-body Hamiltonian processing on quantum processors.
