Kipu Quantum Makes Quantum-Enhanced AI Deployable in Production
Train on a quantum processor, deploy entirely on classical hardware
New hybrid framework removes the single biggest barrier to enterprise adoption of quantum machine learning: the requirement to run a quantum processor for every prediction.
Berlin, Germany — 20 May 2026 — Kipu Quantum today released a new hybrid quantum-classical framework that allows quantum-enhanced machine learning models to be trained on a quantum processor and deployed entirely on classical hardware — at the speed, cost and operational profile that enterprise production pipelines require.
Quantum feature extraction has been delivering measurably richer data representations than classical feature engineering across multiple peer-reviewed studies, validated by Kipu Quantum and others on IBM quantum processors, including IBM Kingston (156 qubits). But the commercial story has been stalling at the same point every time: every prediction required a quantum computer in the loop, an operational requirement no production machine-learning system in financial services, manufacturing, life sciences or geospatial analytics can absorb. The technology worked. The deployment did not.
How the framework works
The quantum processor is used only during a targeted training stage, where it learns the correlations that quantum feature extraction is uniquely good at producing. Those quantum-derived representations are then transferred into a lightweight classical surrogate model. From that point on, deployment is fully classical: microsecond inference latency, retrainable on a normal MLOps cadence, and managed on the same procurement terms as any classical model.
In practice, the quantum processor is run on as little as 20% of the training data — a representative subsample — delivering the same accuracy at one fifth of the quantum hardware cost, a ratio that improves further as data volumes grow. This is possible because quantum feature mappings are stable and reproducible across hardware backends — consistent enough for a classical model to learn the mapping from a manageable set of training examples and generalize reliably at scale.
The role of the quantum computer changes in the process. It stops being an expensive real-time inference engine and is used once, where it adds unique value, then absent from the production system. The predictive lift that quantum feature extraction delivers is preserved. The cost, latency and operational profile of the deployed model collapse to classical.
Demonstrated across commercially significant workloads
The framework has been demonstrated across commercially significant workloads — delivering approximately 10% accuracy improvement on molecular toxicity classification, a 0.932 AUC on medical image diagnostics against a 0.866 ResNet-50 baseline, and 3% on satellite imagery, all over strong classical baselines, with further validation across industrial monitoring, predictive analytics, and customer churn reduction. On a satellite benchmark, the surrogate model matched the full quantum result exactly, achieving 87% accuracy against an 84% classical baseline.
The work is part of Kipu Quantum’s Rimay product suite, within the company’s quantum machine learning platform.
Industry response
Global Quantum Intelligence (GQI)
“Kipu’s off-line surrogate framework achieves economic quantum advantage by capturing the 2–3% absolute accuracy gains of a quantum processor while running inference entirely on classical hardware. By processing only a small representative subsample (e.g., 20%) on actual quantum hardware, the framework reduces expensive quantum executions by a factor of 5 or more. The methodology is actively applied to high-volume enterprise problems, such as satellite drone imagery (TreeSatAI benchmark), medical diagnostics (Breast MedMNIST), and customer intent routing.”
— André König, CEO at Global Quantum Intelligence
NTT DATA
“There is a compelling shift happening in how quantum computing will create value, i.e. not by replacing classical systems, but by teaching them something they could not learn alone. Kipu Quantum’s quantum feature surrogate framework is a masterclass in exactly that — marrying quantum-derived representations with the classical infrastructure enterprises already own and trust. For organizations like NTT DATA, serving critical sectors at global scale, this is the inflection point we’ve been preparing for: measurable accuracy gains, zero quantum dependency at inference, and seamless integration into existing production pipelines. We are ready.”
— Rika Nakazawa, Chief Commercial Innovation at NTT DATA
MOEVE
“Through the Kipu Quantum Hub platform, we are achieving promising milestones that can optimize classical models in image classification for predictive maintenance. The Proof of Concept we implemented delivered positive results by using thermographic drone imagery and adopting hybrid classical-quantum technology for the early detection of issues in our energy parks. Additionally, we have partnered with Kipu Quantum through our Quantum Center of Excellence to analyze mechanical components.”
— Estela Vilches, Head of Digital Innovation at MOEVE
KPMG
“The scope of this technology is intentionally broad and industry-agnostic, providing a scalable solution for a wide range of immediately viable use cases. From satellite image classification and advanced customer analytics to the rapid screening of pharmaceutical candidates, Kipu’s approach allows enterprises to leverage the specific computational advantages of quantum systems across their entire portfolio of data-intensive challenges today.”
— Aaron Kemp, Senior Director Quantum Research & Enterprise Innovation at KPMG US