QUANTUM GRAPH NEURAL NETWORKS FOR TIME PROPAGATION IN CONDENSED MATTER

Date

2024-9-04

Author

Yurtseven, Kaan

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Quantum Graph Neural Networks (QGNN) are a new class of quantum neural network ansatz which are tailored to represent quantum processes which have a graph structure. They have application in simulating Hamiltonian dynamics of quantum systems. The topology of the graph plays an important role, e.g. in inferring stability of steady states. They also manage to reproduce transmission fluctuations and can be generalized to the nonlinear domain. In this work, we adapt QGNN for simulating electronic dynamics in condensed matter systems. We are particulary interested in Fermi-Hubbard model in line with this, we investigated time evolution and scaling of Majonara Hubbard Model. We employ Quantum Autoencoder architecture to be able to simulate fermionic wavefunction in latent space using compressed wavefunctions propagation in time.

Subject Keywords

Quantum Autoencoder, Quantum Graph Neural Networks, Time-evolution, Fermi-Hubbard Model, Majonara Hubbard Model

URI

https://hdl.handle.net/11511/111493

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Graduate School of Natural and Applied Sciences, Thesis