Development of energy-efficient materials/devices for quantum storage


In this work we will deliver highly original solutions to two important problems in our society, i.e., the physical limitations of data-storage platforms, and their booming energy demand.


  1. To design new quantum magnetic devices where energy-efficient operations are developed with optimized computing architectures beyond classical approaches,
  2. To model and improve scalability and intrinsically integrate AI self-learning capabilities within the material at large-scale production, and
  3. To target high density at low-power consumption for multifunctional device-platforms for quantum storage technologies (QSTs)


With the advent of 5G networks, Internet of Things (IoT), particularly in the defence and security area, there is an exponential growth on the amount of data generated worldwide1. It is estimated that by 2025, more than 175 Zettabyte (1 Zettabyte=109 Terabytes) of data per year will be produced. Indeed, information technology is predicted to consume 21% of the world electricity supply by 20302,3.

There is hence a pressing economic and societal urge to find more energy efficient solutions. In order to make progress in the targeted area of quantum storage technologies, new materials and/or processes need to be created or found. Therefore, the proposed project aims to: (i) find the most promising compounds displaying magnetic textures at technologically relevant temperatures, (ii) understand the quantum interactions between electrical currents, femtosecond laser pulses with spin-lattices, (iii) design energy-saving and high-performance quantum computing mechanism for applications in quantum storage technologies (QSTs).


  1. Reinsel, D., Gantz, J., Rydning, J. Data age 2025. The digitalization of the world, Seagate Inc. 2018.
  3. Jones, N. Nature 2018, 561, 113.
  4. D’Aiello, C., Awschalom, D. D., Bernien, H., et al. Quantum Sci. Technol. 2021, 6, 030501.
  5. Khela M. et al. Nat. Commun. 2023, 14, 1378.

Research theme: 

Principal supervisor: 

Professor Rebecca Cheung
University of Edinburgh, School of Engineering

Assistant supervisor: 

Dr Elton Santos
University of Edinburgh, School of Physics and Astronomy

Professor Cristian Bonato
Heriot-Watt University, School of Engineering and Physical Sciences