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Master theses / Diploma theses / Student Assistant

Development of Quantum Generative Adversarial Learning Networks for Large-Scale Applications (Id 344)

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Foto: Quantum Circuit Diagram ©Copyright: Debanjan KonarThe Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. CASUS was founded in 2019 in Görlitz and conducts digital interdisciplinary systems research in various fields such as earth systems research, systems biology, and materials science.

We are looking for motivated, creative, and curious students (f/m/d) to help us in designing and simulating Quantum Generative Adversarial Learning Networks relying on hybrid classical-quantum algorithms for NISQ devices.

The scope of your job:

The Department "Matter under Extreme Conditions" at CASUS investigates how quantum machine learning algorithms can be applied in large-scale applications including material science and computer vision. We particularly work on hybrid-classical quantum algorithms and quantum optimization. In this project, you will investigate the feasibility of Quantum Generative Adversarial Learning Networks using Hybrid classical-quantum algorithms and Variational Quantum Circuits (VQCs). These algorithms rely on a hybrid classical-quantum circuit with gate parameters optimized during training. This involves improving the in-house software and combining it with larger software suites. Besides ease of use, another focus of these workflows should be reproducibility. Prior knowledge of quantum machine learning algorithm simulation is required!

Tasks for this project might involve

  • Literature research on existing solutions for the simulations of Quantum Generative Adversarial Learning Networks.
  • Development and improvement of the existing Quantum Generative Adversarial Learning (QuanGAN) Networks using the PennyLane Quantum Simulator.
  • Development of the learning procedure for QuanGAN for representing the probability distribution underlying large datasets and encoding them as a quantum state.
  • Integration of existing workflows in larger software suites in Python.

Institute: CASUS

Contact: Konar, Debanjan, Dr. Cangi, Attila

Requirements

● Bachelor/Master candidate in computer science or a related field.
● Experience with Machine learning, Deep learning, Python, IBM Q (Qiskit), PennyLane Quantum Simulator, PyTorch library.
● Ability and motivation to work in a team.
● Good language skills in English.
● Experience with scientific software development (optional).

Conditions

● A vibrant research community in an open, diverse, and international work environment.
● Scientific excellence and extensive professional networking opportunities.
● Compensation as student researcher (working hours to be determined).