KCDS Virtual Open House 2023 - Fall
KCDS PhD Project 06: Trainability of data driven quantum models
MATH PI: Dr. Leonid Chaichenets, Steinbuch Centre for Computing (SCC), Scientific Computing & Mathematics (SCC-SCM)
SEE PI: Dr.-Ing. Eileen Kühn, Steinbuch Centre for Computing (SCC), Data Analytics, Access and Applications (SCC-D3A)
Department(s): Informatics (Computer Science) or Mathematics
Type of position: 75% FTE, TV-L E13
In the field of quantum machine learning many ansätze for designing quantum circuits that make up a trainable quantum model are influenced by heuristics but also by current challenges of quantum computers such as noise and size. One influential paper presents a collection of potential hardware-efficient building blocks for quantum circuits analyzing those regarding trainability and their efficiency to benefit from the available problem space of a quantum computer. Typical scientific questions thus involve deciding for one of the building blocks, and the number of repetitions required to solve the underlying problem. To answer these questions several experiments are required. This contrasts with current developments in geometric quantum machine learning, exploiting symmetries in data to be encoded as part of the quantum circuit. Based on these symmetries that effectively limit the search space, the learning process can become more efficient in terms of computing resources, but also in terms of time. Further questions about the choice of ansatz or number of repetitions can be omitted in the best case.
In this project, varying datasets shall be reviewed with regard to their features and correlations to identify a new set of building blocks to minimize the number of experiments required while respecting the challenges of today’s quantum computers.
Requirements for this position:
- Knowledge of machine learning frameworks (e.g. PyTorch, TensorFlow)
- Good programming skills (e.g. Python)
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