Controlling Information Transfer in Spintronics Networks
The propagation of information encoded in spin degrees of freedom through networks of coupled spins enables important applications in spintronics and quantum information processing. Control is required to direct the flow of information through a spintronic network in an efficient manner, e.g for an on-chip interconnect or for routing quantum information. In principle information stored in spin states can propagate through a network of coupled spins without any charge transport. As propagation of spin-based information is governed by quantum-mechanics and the Schrodinger equation, excitations in a spin network propagate, disperse and refocus in a wave-like manner. We study control of information propagation in rings of spins as a simple prototype of a router for spin-based information. For our purposes we restrict ourselves to spin-1/2 particles with uniform nearest neighbour couplings forming a ring with a single excitation (or one bit) in the network. Control can be utilised to maximise transfer efficiency and speed of this excitation. We specifically consider optimising spatially distributed potentials, which remain constant during the evolution, in contrast to dynamic control schemes, which require dynamic modulation or fast switching of the control potentials. Due to the limited degrees of freedom in the system, finding a control that maximises the transfer probability in a short time is difficult, but in principle simplifies the implementation of the routing scheme. For practical implementation of such a scheme specific network structures and spin coupling strengths will have to be identified from measurements of actual devices to build a model suitable to find the necessary controls. For this we present an approach for discriminating between different network structures and learning model parameters.
Frank C Langbein is a lecturer in computer science at Cardiff University, leading the Quantum Technologies Group, and is a member of the Geometric Computing and Computer Vision research group. He is also a member of the Research Institute for Visual Computing where he is co-leading the sub-programme on vision-based geometric modelling and the interface with science. He received a diploma in mathematics from Stuttgart University in 1998 and a PhD from Cardiff University in 2003. He is working on computational and geometric modelling, control, machine learning and visual computing with applications in quantum technologies, healthcare, mechanical and chemical engineering and spintronics.
Prof. Edmond Jonckheere