Our company specializes in Quantum Circuit Compilation (Qubiter) and applying the evolving Quantum Computing Resources for Bayesian Network modeling (Quantum Fog), which is a concept that finds numerous data driven use cases in business, engineering and science.
Unlike conventional deep learning approaches to AI, Bayesian Networks represent a way to directly extract knowledge, such as statistically validated cause-effect relationships, from the underlying data.
With our software stack our customers are Quantum Ready, giving them a decisive competitive advantage, as they will be able to leverage Quantum Supremacy as soon as the hardware reaches this critical inflection point.
We develop our software so that, once available, the quantum computing acceleration of analytical pipelines, built with our product, will be completely transparent to our users. I.e., there will be no additional development burden. The Bayesian Network training task can simply be redirected to a Cloud based or in-house quantum computing resource.
The following figure illustrates how the modules fit into an overall Quantum computing toolchain:
At this time we can compile to IBM's QUISKit circuit model, Google's Cirq as well as integrate with Rigetti's pyQuil.
We always welcome inquiries from new entries in the QC hardware field to widen the QC platforms we can support.