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.

Prof. Mio Murao from the University of Tokyo delivered a landmark talk on higher order quantum algorithms for learning quantum objects at a recent IVADO seminar in Montreal.