While we've tried various "hybridizations" of quantum binary decision diagrams (QBDD) with state vector simulation and other methods in the past, they as of yet have not given any compelling practical advantages in general use cases, as best the Qrack team can assess.
Trying a slightly different approach with the new QBdtHybrid, this might remain the case, like for the quantum Fourier transform or random circuit sampling. However, holistic integration with the overall "layer stack" seems significantly improved, with this approach, and special-case advantages might improved. (We still need to collect additional benchmark data, to test this.)
While we've tried various "hybridizations" of quantum binary decision diagrams (QBDD) with state vector simulation and other methods in the past, they as of yet have not given any compelling practical advantages in general use cases, as best the Qrack team can assess.
Trying a slightly different approach with the new
QBdtHybrid, this might remain the case, like for the quantum Fourier transform or random circuit sampling. However, holistic integration with the overall "layer stack" seems significantly improved, with this approach, and special-case advantages might improved. (We still need to collect additional benchmark data, to test this.)