The research program proposes an explorative approach for a research roadmap in Quantum Machine Learning, with specific focus on algorithms related to the field of computer vision. Methodologically, the project will explore hybrid classic-quantum architectures, and quantum inspired approaches. Examples of lines of work are the design of efficient quantum generative systems or the use of quantum algorithms to solve classical graph theory problems. However, the position is open to specific research interests oriented to solving problems with hard scalability in classical scenarios, but with potential alternative approaches in the quantum computing realm. The research action will be developed in 4-year funded research project in close collaboration with Spanish Institutions from CSIC such CINN for experimental deployment, INMA for theoretical modeling, ICMM for quantum algorithms methodological support, and Eurecat for industrial connections. The activity is contextualized in the priorities of the QML-CVC Group, which tackles fundamental research questions such as the role of Machine Learning in a quantum world, the identification of hard classical tasks that could be solved in a practical way with quantum technology, and the research on unveiled social impacts of Quantum Technologies and Artificial Intelligence.
Available Infrastructures:
- Computer Vision Center’s GPU HPC Cluster.
- Access to PIC HPC facility at UAB.
- Access to 34 qubits Quantum Simulator (in agreement with Eurecat)..
Possible Secondments:
Eurecat, UAB, etc.
Keywords
Quantum Machine Learning, Quantum Computing, Variational Algorithms, Hybrid Algorithms, Generative Quantum Machine Learning.
Principal Investigator
Fernando Vilariño leads research on Quantum Machine Learning.
Email: fernando@cvc.uab.es
Web: http://qml.cvc.uab.es
