QC Ware has added API’s to perform linear algebra library modules to their Forge software. These routines include matrix multiplication and distance estimation and can be used with the data loader library module that we reported on last year. These modules are all useful for implementation of quantum machine learning applications.
QC Ware has a strategy of differentiating itself by providing its customers with turnkey quantum algorithms, such as these, which can provide better performance and make it easier for customers to develop applications. They have similar efforts to create unique algorithms that can be used in the areas of binary optimization, chemical simulation, differential equations and Monte Carlo simulations.
QC Ware’s Forge supports backends from D-Wave, IonQ, and Rigetti through Amazon Braket, although the linear algebra modules would require the gate based machines so cannot be used with D-Wave’s quantum annealer. Forge also support IBM’s Q Systems for those customers who provide their own IBM credentials. Simulations can be run on Amazon’s Braket simulators, IBM’s Q System simulators, Nvidia GPUs, or QC Ware’s own simulators.
More information about QC Ware’s support for linear algebra APIs can be found in their news release here.