Ph.D. opening, fully funded, on "Hybrid Quantum Annealer Algorithms for Tabu Search and Data-Driven Computation" at University of Trento, Italy

Job type: 


Application deadline: 

Monday, June 15, 2020

Research group: 

Quantum Information and Computing, research program at Department of Information Engineering and Computer Science, University of Trento, Italy

PhD Project: Hybrid Quantum Annealer Algorithms for Tabu Search and Data-Driven Computation

Fully funded position within ICT Doctoral school, University of Trento, Italy

Location: Department of Engineering and Computer Science DISI, University of Trento, Italy in collaboration with German Aerospace Center (DLR) Köln, Germany

Description: The current availability of limited quantum hardware requires to devise hybrid quantum-classical algorithms that take advantage of the existing hardware and overcome their limitations by combining classical and quantum computation. One of the existing hardware, the D-Wave quantum annealer, solves optimization problems however it suffers from limitations in the actual encoding of the target functions into the architecture. A novel quantum-classical technique is based on a local search where already-visited solutions are penalized to avoid a redundant exploration of the solution space (Tabu Search) and inducing a learning mechanism of the problem encoding into the quantum architecture. The learning search is implemented in the quantum annealer by a sequence of re-initializations of the Ising Hamiltonian of the qubit network iterated within the hybrid quantum-classical structure. An algorithm of Quantum Annealing Learning Search (QALS) has been proposed in a recent paper (D. Pastorello E. Blanzieri. Quantum Annealing Learning Search for solving QUBO problems. Quantum Information Processing 18:303 (2019), see also
The PhD candidate will implement and test hybrid quantum-classical algorithms that can run on a quantum annealer. Initially, the PhD program will focus on the existing QALS algorithm based on tabu search for solving Quadratic Unconstrained Binary Optimization problems. The expected result is a complexity-characterized, implemented and empirically evaluated learning search quantum-classical algorithm for quantum annealers. In a second phase the PhD activity will extend to optimization of more general target functions and also towards data representation into the quantum annealer for data-driven computation.


Master's Degree in Computer Science, Mathematics, or Physics.

Familiarity with: Algorithm Theory, Programming (Python experience preferred), Linear algebra

Experience in one of the following areas would also be appreciated: Quantum Mechanics, Ising model, Quantum Computing, Quantum Annealing, Optimization algorithms, Tabu search.

Application DEADLINE June 15 2020

Research program at Department of Engineering and Computer Science