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Hybrid Quantum Algorithms for Partial Differential
hace 2 semanas
The aim if this project is to explore new ideas in the field of quantum and quantum-inspired (tensor network) machine learning and optimization methods to develop hybrid algorithms (classical & quantum) to solve Partial Differential Equations (PDEs).
This includes, among others, exploring and developing more efficient algorithms for fluid dynamics, among others, improving the overall performance in time and energy cost, as well as in accuracy.
The hybrid algorithms will make use of different QPUs in combination with state of the art Tensor Network methods. The research will be carried in collaboration with Multiverse Computing, in the context of the CUCO project.It's required a PhD in quantum computing or quantum-inspired numerical simulation methods.
Contract duration: 1 year (possibility to extend up to 3 years)
Target start date: 01/09/2023
Reference: 2023/58
Application deadline: 15/06/2023
Funds:
This project has received funding from the MCIN program "Severo Ochoa", under reference AEI/ CEX S.
Reference letters are welcome but not indispensable.
Deadline:
June 15, 2023
Job category:
Postdoc
Area:
Maths, Experimental Sciences and Engineering
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