Variational quantum algorithms (VQAs) have consolidated as a tool in quantum information in noisy, intermediate-scale quantum hardware, trading circuit depth for classical-quantum feedback. This Collection, curated by our Associate Editor Keisuke Fujii, showcases some of the relevant papers published in this area in our journal. From redesigning the ansatz itself to pushing quantum approximate optimization toward large, noisy hardware; from adapting variational time-evolution to simulate gauge theories, batteries, and spin chains, to squeezing more signal from every measurement via joint readouts, ensemble learning, and machine-learned noise models.
