Quantum software engineering developer Classiq and the Pontificia Universidad Católica de Chile (UC Chile) have launched a joint 12-month research initiative to engineer hybrid quantum-classical machine learning algorithms for advanced biomedical image analysis. Funded through the Avanza UC 2025 competition, the project—titled “Enhancing Pathology through Quantum Computing”—establishes Latin America’s inaugural computational pathology consortium. The project integrates Classiq’s automated circuit synthesis platform with NVIDIA’s CUDA-Q hybrid infrastructure and draws on curated histopathology datasets provided by Brazilian research institutions, including the Fundação Oswaldo Cruz (FIOCRUZ) and the Universidade Federal da Bahia (UFBA).
Technical Architecture & Quantum Machine Learning Workflows
The co-design roadmap addresses the high dimensionality and feature complexity of whole-slide tissue images, which strain classical computer vision architectures during pixel-level segmentation tasks. Rather than relying entirely on deep classical neural networks, the project develops hybrid Quantum Machine Learning (QML) pipelines optimized for renal pathology. The research team uses Classiq’s abstract functional modeling environment to automatically synthesize and optimize specialized quantum network topologies, bypassing manual gate-level programming boundaries.
The joint computational pathology workflow focuses on three clinical analysis targets:
The Qubit Report
- Quantum Convolutional Neural Networks (QCNNs): Adapting quantum convolutional layers to compress high-resolution structural features, optimizing automated glomerular segmentation across complex tissue samples. The Qubit Report
- Variational Quantum Classifiers (VQCs): Applying parameterized, variational quantum logic states to execute multi-class kidney lesion classification models. The Qubit Report
- Quantum Kernel Methods: Utilizing high-dimensional quantum state spaces to perform semantic pattern searches, isolating subtle diagnostic anomalies within dense histological slides. GlobeNewswire
The compiled software stack executes via a unified runtime environment. The hybrid algorithms are compiled using the NVIDIA CUDA-Q platform, enabling low-latency coprocessor data routing. This framework allows the team to run high-fidelity algorithmic simulations on classical NVIDIA AI supercomputing infrastructure before offloading the optimized, hardware-ready circuits to IonQ’s trapped-ion quantum processing units (QPUs) for physical benchmark testing.
Regional Strategy & Institutional Integration
The partnership establishes an operational anchor for advanced computing applications within the South American medical tech sector, directly aligning with Chile’s National Strategy for Quantum Technologies 2025–2035. Directed by Dr. Dardo Goyeneche of the UC Chile Faculty of Physics—who also leads the QuDIT research group and Project QuAntü, Chile’s universal quantum computer construction initiative—and supported by Dr. Daniel Uzcátegui of the Universidad Católica de la Santísima Concepción (UCSC), the project is designed to cross-train local engineering talent on global software deployment workflows. By embedding Classiq’s hardware-agnostic coding layer into regional healthcare pipelines, the initiative creates a validated framework to port emerging quantum advantages directly into active public health diagnostic tools.
The official consortium launch release can be reviewed via the Classiq insights repository here.
June 5, 2026
