Berlin / Chicago, November 12, 2026 -- Kipu Quantum today announced that Rimay, its quantum feature extraction technology for machine learning, will be available through IBM Quantum Functions Catalog, giving IBM Quantum Flex and Premium Plan users a direct way to apply Kipu’s quantum machine learning capabilities within the IBM Quantum Platform.

The launch extends Kipu Quantum’s presence on the IBM Quantum Platform, from optimization into quantum machine learning. Kipu’s Iskay Quantum Optimizer is already available in the IBM Quantum Functions Catalog. Users can submit classical optimization problems and execute Kipu’s compressed quantum algorithms on IBM quantum computers.

Rimay addresses a different challenge: improving the information available to machine learning models. Rather than replacing an organisation’s existing model or ML stack, Rimay adds a quantum feature extraction step. Classical data is processed using quantum feature extraction to identify additional features and correlations before the enriched data is returned to conventional machine learning models.

Rimay is designed for data science teams working with tabular datasets where prediction quality has plateaued. It slots into existing machine learning pipelines as a drop-in automated feature extraction algorithm. It encodes training data into a Hamiltonian, which then maps each feature to a qubit, and runs a counterdiabatic evolution on IBM’s quantum computers to generate quantum-transformed features.

Users can then feed the quantum-transformed features into their existing classical models, either on their own or alongside the original features.

In one recent deployment with a Fortune 100 company, Rimay was used to enhance an existing customer-intent prediction model using tabular data on the latest IBM Quantum Nighthawk processor. The quantum-enhanced workflow improved prediction performance while integrating with the customer’s existing classical machine learning stack, with the potential to deliver significant operational savings.

Alongside the launch of Rimay’s availability in the IBM Quantum Functions Catalog, users will have access to a credit-risk classification tutorial, included in IBM Quantum documentation, which will show how to prepare tabular data, fit a quantum feature map on IBM quantum computers, and use the resulting features within a conventional machine learning workflow.