Academic Publications

Summary: This paper resulted from a project that Guus and I developed in conjunction with engineers from Deutsche Telekom (DT), Europe's largest telecommunications provider. DT has plans of deploying a trusted-repeater network, and was interested in finding out how it could later be upgraded to a full-on quantum-repeater network. To answer this question, we determined hardware requirements to perform quantum key distribution and blind quantum computing.

Summary: My main contribution here was to help apply the optimization procedure we had introduced in 'Optimizing entanglement generation and distribution using genetic algorithms' to a new use case: blind quantum computing.

Summary: This publication was the result of the work done together with my first master's student, Adria. He used the approach we introduced in 'Optimizing entanglement generation and distribution using genetic algorithms' to co-optimize hardware parameters and entanglement generation and distribution protocols. My contribution here was to conceive the project and guide Adria.

Summary: This project, co-led by Guus and I, was at the time and to the best of my knowledge, the most detailed study on making quantum repeaters 'useful'. It investigated hardware requirements for different physical platforms deployed on a real-world fiber grid for a (small instances of a) practical quantum-networking application, namely blind quantum computing.

Summary: My contribution here was minor, and dates back to my time as a master student in Lisbon in 2018-2019. I benchmarked the energy use of different classical information processing devices. This work appears in Section IB and Appendix I of the paper.

Summary: My contribution here was to implement the code to perform what in this paper is called 'backward benchmarking', the method for determining hardware requirements we introduced in 'Optimizing entanglement generation and distribution using genetic algorithms' and apply it to a simulation of a quantum money protocol.

Summary: This was the first paper of my PhD, and resulted from work developing a methodology for determining hardware requirements for quantum-networking applications integrating cost-function design, quantum-network simulations using the NetSquid simulator and genetic algorithms. This methodology was then applied (by me and others) to multiple other quantum-networking scenarios.

Summary: This paper resulted from part of my work as a master's student. The ultimate goal was to design a quantum neural network based on a then-recent proposal of a quantum memristor. The first step towards that was to design a memristor-based classical neural network - which is what this paper reports on. The main innovation here is that both neurons and synapses are implemented with memristors, whereas to the best of my knowledge all previous proposals employed memristors for the neurons only. The next step would have been to quantize this network, and while I did produce some results in that direction, they have never been published.