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    PhD student position in AI-driven optimization of variational algorithms for near-term quantum device applications in quantum chemistry - Gothenburg, Sverige - Chalmers tekniska högskola

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    Variational quantum algorithms (VQAs) seek to make the most of noisy intermediate-scale quantum (NISQ) devices currently operating at academic and industrial labs around the world.

    VQAs are hybrid algorithms where NISQ devices are controlled via optimization algorithms implemented on classical computers. VQA algorithms have promising near-term applications including in quantum chemistry and optimization.

    The application of VQAs to quantum chemical problems is currently limited by both software and hardware – in this project the selected candidate will address two key limitations on the software side:

    • Warm-start for VQAs using the underlying structure of molecules and their Hamiltonians – to minimize the number of quantum-classical computer cycles needed for convergence.
    • Learning functions to map molecules to bespoke hardwareefficient circuit Ansätze – to lower the demand on the number of qubits necessary to accurately compute ground state energies.

    The selected candidate will be embedded into an interdisciplinary environment between the Chalmers departments of as well as , and the (WACQT).

    This inter-disciplinary research project is part of an on-going collaboration between the groups of Simon Olsson, Anton Frisk Kockum, and Martin Rahm to enable quantum chemistry on quantum computers aided by machine learning.

    The selected candidate will be based at the Computer Science and Engineering department yet enjoy full access to and a work-place at WACQT and the Chemistry and Chemical Engineering departments.

    The research takes place at Chalmers University of Technology, and the position is funded by Wallenberg AI, Autonomous Systems and Software Program (described below) and the (WACQT).


    About the Wallenberg Center for Quantum Technology - WACQT
    is a 12-year initiative aimed to bring Swedish academia and industry to the forefront of Quantum Technology.

    The center is funded mainly by the Knut and Alice Wallenberg Foundations with additional contributions from participating universities and industry.

    At Chalmers, the focus is to build a superconducting quantum computer and simulator and to explore useful applications of quantum computing.

    The Quantum Technology and Applied Quantum Physics laboratories work on experimental and theoretical aspects of quantum information processing with superconducting electronics.

    We currently have a transmon-based 25-qubit quantum processor and are scaling up further. As a researcher in this project, you will be closely interacting with postdocs and PhD students in WACQT. WACQT is committed to promoting career development, diversity, and gender equality through networking and supporting activities.

    About the Wallenberg AI, Autonomous Systems and Software Program – WASP

    WASP is Sweden's largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment.

    The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems.

    The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.


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    Major responsibilities As a Ph.
    D.

    student, you will enroll in a graduate program at the Department of Computer Science and Engineering, where your primary responsibility is to pursue your doctoral studies, which entails:


    • 80% Research and course work
    • 20% Service, including teaching.
    The research entails developing and implementing your scientific ideas, communicating your results orally or in written form. Service entails being a teaching assistant in Chalmers' undergraduate and masters-level courses or performing other departmental tasks.


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