At CTIC Quantum Lab we work to take quantum algorithms beyond theory and explore their application in real problems. To do so, we focus on two main lines:
Quantum Machine Learning (QML)
We explore the combination of quantum computing and artificial intelligence, investigating how properties such as superposition and entanglement in quantum models can complement or enhance the scope of traditional AI. We currently address the following applications:
- Artificial Vision: image classification and complex pattern recognition, with applications in diagnostics, quality control or scientific data analysis.
- Natural Language Processing (NLP): new ways of representing linguistic information to improve understanding of context and ambiguity in large volumes of text.
- Anomaly detection and cybersecurity: identification of irregular patterns and subtle threats in massive data streams, drastically reducing false positives and strengthening transaction security in critical infrastructures.
- Time series prediction: improving accuracy in models to anticipate the evolution of variables such as energy demand, markets or natural phenomena.
- Generative IA: exploration of quantum generative models (such as QGANs) for materials design, molecular simulation or synthetic data generation.
Quantum optimization
We investigate algorithms oriented to complex optimization problems, where the number of variables makes it difficult to solve them with classical methods. Some application cases are:
- Logistics and Supply Chain: solving the "commuter problem" on a large scale to optimize last mile delivery routes, fleet management and strategic location of warehouses, reducing costs and carbon footprint.
- Energy: efficient management of power grids, optimizing distribution, storage and renewable integration for supply stability.
- Industry: production planning, allocating resources and machinery in the most efficient way to minimize downtime and bottlenecks.
