Research &Development

The development of quantum machine learning (QML) models focuses on creating hybrid algorithms that harness quantum circuits to enhance pattern recognition, feature extraction, and high-dimensional data processing beyond classical limits. This includes exploring variational quantum algorithms, quantum kernels, and quantum generative models that can be deployed across simulators and emerging quantum hardware. The goal is to build a scalable QML framework that accelerates AI tasks in domains such as classification, anomaly detection, and complex decision-making while producing reusable models for research and industry adoption.

Quantum-accelerated optimisation leverages quantum algorithms—such as QAOA, VQE-based optimisation, and quantum annealing—to solve large, computationally intensive optimisation problems more efficiently than classical approaches. The work involves designing hybrid pipelines that allocate the most challenging combinatorial or high-dimensional components to quantum backends while maintaining classical oversight for scalability and practicality. These capabilities enable significant improvements in routing, scheduling, resource allocation, and portfolio optimisation tasks, laying the foundation for early quantum advantage in industrial operations.

AI-driven chemical and materials simulation combines machine learning with quantum algorithms to model molecular systems, reaction pathways, and material properties with higher fidelity and reduced computational cost. By integrating QML-based Hamiltonian estimation, quantum-enhanced simulation methods, and classical AI surrogates, the platform enables accelerated discovery of catalysts, batteries, semiconductors, pharmaceuticals, and advanced materials. This capability supports Malaysia’s high-tech industries by shortening R&D cycles, improving prediction accuracy, and enabling breakthroughs in energy, manufacturing, and healthcare technologies.

This component focuses on developing applied quantum-AI solutions tailored to national priority sectors—including logistics optimisation, renewable energy forecasting, financial risk modelling, and defence-grade sensing and decision systems. Each use case is designed as a hybrid model where classical AI provides scalable inference while quantum components deliver enhanced optimisation, simulation, or data encoding advantages. These sector-specific implementations demonstrate real economic and strategic impact, ensuring quantum technologies transition from research to high-value, deployable applications.

Industry plug-in modules delivered via APIs provide a flexible, enterprise-ready interface that allows companies to integrate quantum-AI capabilities directly into their existing software and operational workflows. These modules encapsulate QML models, quantum optimisation routines, and simulation engines into standardised, secure API endpoints compatible with cloud and on-premise deployments. This approach accelerates industrial adoption by simplifying integration, lowering technical barriers, and enabling businesses to access quantum-enhanced tools without needing deep quantum expertise.