Classiq and UC Chile have started a 12-month research program that creates Latin America’s first computational pathology consortium, aimed at biomedical image analysis with hybrid quantum-classical machine learning.
The project targets renal pathology, where whole-slide tissue images overwhelm classical pixel-level segmentation systems with high-dimensional, feature-rich data.
Classiq’s circuit-synthesis platform will pair with NVIDIA CUDA-Q to simulate and compile hybrid workflows before sending optimized circuits to IonQ trapped-ion quantum processors for benchmark tests.
Brazilian datasets from FIOCRUZ and UFBA will support work on glomerular segmentation, kidney lesion classification and anomaly detection using QCNNs, variational classifiers and quantum kernel methods.
The consortium also supports Chile’s 2025-2035 quantum strategy by building a regional medical-tech base and training local engineers to deploy quantum tools in public-health diagnostics.
How can quantum computers analyze massive medical images when they have so few qubits available today?
Will this quantum leap in diagnostics bridge Latin America's healthcare gap or create a new technological divide?
Quantum Machine Learning for Renal Pathology: The Classiq-UC Chile Project and Its Role in Chile’s National Quantum Strategy 2025–2035
Overview
The recently launched 'Enhancing Pathology through Quantum Computing' project is a 12-month collaborative initiative led by Dr. Goyeneche, bringing together Classiq and researchers from Chile and Brazil. This project marks a significant step in applying advanced computational methods, specifically quantum machine learning, to address critical biomedical challenges. Its main goal is to connect fundamental quantum research with urgent healthcare needs, focusing on renal pathology—a growing public health concern in Chile and Latin America. By leveraging quantum machine learning, the project aims to develop innovative solutions for complex diagnostic tasks in pathology, demonstrating the real-world impact of quantum technologies in medicine.