A new partnership between ORIENTOM and Fondazione LINKS will evaluate quantum and quantum-inspired algorithms for financial modeling, risk analysis, and machine learning, using hybrid quantum-classical workflows and high-performance computing infrastructure
ORIENTOM, a quantum software developer based in Seoul, and the Italian research institution Fondazione LINKS have announced a research partnership focused on integrating quantum computing and high-performance computing (HPC) for financial applications. The collaboration, formalized through a Memorandum of Understanding, aims to assess the technical feasibility of quantum and quantum-inspired algorithms in commercial finance, banking, and insurance workflows.
Hybrid Quantum-Classical Integration
The joint initiative will combine ORIENTOM's hardware-agnostic Quantum Middleware platform with Fondazione LINKS' HPC infrastructure and quantum research environment. This environment is supported by local partners including Politecnico di Torino and the Italian National Institute for Metrology (INRiM). The partnership will develop proof-of-concept frameworks and operational prototypes to test quantum algorithms on real-world financial problems, with a focus on hybrid workflows that leverage both classical and quantum resources.
Targeted Financial Use Cases
The collaboration will address three primary use cases: derivatives and options pricing, portfolio optimization and risk management, and machine learning for banking and insurance. For derivatives pricing, the teams plan to accelerate high-dimensional stochastic models by integrating classical Monte Carlo simulations with Quantum Amplitude Estimation (QAE), a quantum algorithm that can theoretically reduce the number of required samples. In portfolio optimization, quantum and quantum-inspired algorithms will be applied to complex combinatorial problems such as portfolio liquidation, order execution, and real-time risk analysis. The machine learning component will explore quantum and quantum-inspired models for tasks including credit default prediction, fraud detection, and personalized product recommendations.
Technical Benchmarks and Research Scope
Led by ORIENTOM CEO Alfred Bang, the partnership will establish benchmark methodologies to evaluate performance gains across hybrid classical-quantum workflows. The teams will measure computational speed, accuracy, and resource requirements for each use case, comparing quantum-enhanced approaches with established classical methods. While the collaboration is at the proof-of-concept stage, the partners intend to pursue joint research grants, including bilateral Korea-Europe initiatives, and to explore the extension of quantum optimization algorithms to adjacent sectors such as smart grid management and energy systems.
Comparative Context and Industry Trends
This partnership reflects a broader trend of integrating quantum computing with classical HPC to address computational bottlenecks in finance and other data-intensive industries. Similar efforts are underway in quantum networking hardware, as seen in the recent collaboration between Qunnect and Monarch Quantum to develop modular entanglement distribution systems for scalable quantum networks. For more on developments in quantum hardware integration, see our coverage of emerging quantum network hardware partnerships.
At present, the practical advantage of quantum algorithms in finance remains unproven, with most demonstrations limited to small-scale simulations or laboratory prototypes. The effectiveness of quantum approaches depends on the quality of the quantum hardware, the efficiency of hybrid integration, and the ability to benchmark against optimized classical algorithms. The ORIENTOM and Fondazione LINKS partnership will provide new data on these questions as their prototypes are tested in realistic financial scenarios.
Quantum algorithms such as Quantum Amplitude Estimation (QAE) are designed to accelerate specific computational tasks by exploiting quantum superposition and interference. In practice, the benefit of these algorithms depends on the ability to prepare and measure quantum states with high fidelity, manage noise and decoherence, and integrate quantum subroutines into classical workflows. Hybrid quantum-classical computing refers to architectures where quantum processors handle selected subproblems while classical computers manage the overall workflow, data preprocessing, and postprocessing. The challenge is to identify tasks where quantum resources provide a measurable advantage over the best available classical methods, given current hardware limitations and error rates.
