IBM Qiskit Showcases Faster Quantum Computing with Sample-Based Algorithms

IBM Quantum has unveiled advancements in quantum computing efficiency by highlighting the power of Sample-Based Quantum Diagonalization (SQD) in their latest Qiskit learning module. SQD, a hybrid quantum-classical algorithm, offers a scalable solution for tackling large matrix eigenvalue problems critical to science and industry. The method leverages quantum measurement to project complex problems into a reduced subspace, allowing classical computers to perform diagonalization on a much smaller scale while maintaining accuracy.

Unlike methods such as Variational Quantum Eigensolvers (VQE), SQD uses quantum sampling to define a subspace that supports the ground state of complex systems. This approach enables iterative projection and diagonalization, significantly speeding up computations and making the process more resilient to noise and errors. Researchers can further enhance results with “configuration recovery,” a process that probabilistically corrects errors in measured quantum states—ensuring that computations conform to meaningful physical constraints, such as conservation of particle numbers.

The SQD technique demonstrates superior scalability, robustness, and accuracy in estimating ground states and energies, marking a major step toward practical, real-world quantum computing applications.

Source: “Faster Quantum Computing with Sample-based Algorithms | SQD in Action,” IBM Qiskit, YouTube, Nov 2025