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          <dc:title>Resource distribution, percolation, and topology in quantum networks</dc:title>
          <dc:creator>Conall Campbell (24304448)</dc:creator>
          <dc:subject>PUREID: 661376391</dc:subject>
          <dc:subject>Quantum networks</dc:subject>
          <dc:subject>topology</dc:subject>
          <dc:subject>resource distribution</dc:subject>
          <dc:subject>percolation</dc:subject>
          <dc:description>Quantum networks form the bedrock of emerging quantum technologies. By connecting quantum systems through entangled links, they enable remarkable applications such as distributed quantum computing, secure quantum communication, and entanglement-enhanced sensing. This thesis investigates several key challenges in quantum networks, namely resource distribution, percolation, and topology. The first part of the thesis analyses the limitations of entanglement distribution using separable states (EDSS) in the zero-added-loss photon multiplexing (ZALM) architecture. The study determines that EDSS protocols outperform direct entanglement distribution when only the separable carrier is sent through a noisy channel. However, when noise is considered in channels via transmission to the memories, this advantage is lost. The second part of the thesis investigates link-level state generation in a distillable multiplexed quantum repeater architecture.  Three different photon encodings are compared for two distinct distillation protocols, and the results demonstrate that for the low-loss regime, the GKP photon encoding yields a higher distillable entanglement compared than both Single Rail and Dual Rail photon encodings, regardless of the distillation protocol. The final part of the thesis investigates the performance of an optimised genetic algorithm that infers the topology of an unknown quantum network. Operating in the framework of continuous-time quantum walks and using an externally attached sink probe, the algorithm can successfully reconstruct network topologies of up to 10 nodes. Moreover, when the number of probe sites is increased, the reconstruction process is significantly simplified, and the algorithm is capable of reliably reconstructing network topologies of up to 14 nodes.</dc:description>
          <dc:date>2026-10-01T16:10:14Z</dc:date>
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