Genetic algorithms for quantum spin networks

Spin networks have been proposed as models to achieve many useful functions for quantum devices, including quantum state transfer, quantum gates, quantum-state routing, etc., see e.g. [1],[2]. However, to achieve a desired functionality, selecting a suitable network topology, and calibration of spin-spin interaction energies, and other system parameters are required. Current technology already permits a very good control of these parameters in some of the potential hardware, but determining their suitable values and network topologies for the request functionalities remains a task whose difficulty increases exponentially with the network size. To complicate matters, solutions can be counter-intuitive. To circumvent this, we have proposed the use of tools common in evolutionary computation, namely genetic algorithms, to design quantum devices based on spin networks [3].

Our algorithm is general and could be applied, in principle to any given network and functionality. In [3] we proposed some limited examples of its application. In this project we aim to extend its use to more challenging problems, including multi-qubit gates and/or multi-excitation problems. The research will include studying the algorithm limitations and scaling as well as developing the algorithm itself within the genetic algorithm family and beyond.

Candidates are required to have a strong background in computational physics, including development of their own codes, as well as a solid background in quantum mechanics and/or AI methods. This PhD project is part of an ongoing collaborative research programme between Profs Irene D’Amico and Tim Spiller at York, with Dr Marta Estarellas at Qilimanjaro Quantum Tech, Barcelona, Spain.

1. Unitary Design of Quantum Spin Networks for Robust Routing, Entanglement Generation, and Phase Sensing, Abdulsalam H. Alsulami, Irene D'Amico, Marta P. Estarellas, Timothy P. Spiller, Adv. Quantum Technol. 2200013 (2022), https://arxiv.org/abs/2202.02632

2. Generation and Robustness of Quantum Entanglement in Spin Graphs, Jan Riegelmeyer, Dan Wignall, Marta P. Estarellas, Irene D'Amico, Timothy P. Spiller, Quantum Inf Process 20, 2 (2021), https://arxiv.org/abs/2002.07683

3. Evolutionary computation for adaptive quantum device design, Luke Mortimer, Marta P. Estarellas, Timothy P. Spiller, Irene D'Amico, Adv. Quantum Technol. 2100013 (2021), https://arxiv.org/abs/2009.01706 .

How to apply:

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website https://www.york.ac.uk/physics-engineering-technology/study/funding/ for details about funding opportunities at York.