Prediction of viscosity behavior in oxide glass materials using cation fingerprints with artificial neural networks.

We propose a novel descriptor of materials, named 'cation fingerprints', based on the chemical formula or concentrations of raw materials and their respective properties. To test its performance, this method was used to predict the viscosity of glass materials using the experimental database INTERGLAD. Using artificial neural network models, we succeeded in predicting the temperature required for glass to have a specific viscosity within a root-mean-square error of 33.0°C. We were also able to evaluate the effect of particular target raw materials using a model trained without including the specific target raw material. The results show that cation fingerprints with a neural network model can predict some unseen combinations of raw materials. In addition, we propose a method for estimating the prediction accuracy by calculating cosine similarity of the input features of the material which we want to predict.

Hybridizing ferromagnetic magnons and microwave photons in the quantum limit.

We demonstrate large normal-mode splitting between a magnetostatic mode (the Kittel mode) in a ferromagnetic sphere of yttrium iron garnet and a microwave cavity mode. Strong coupling is achieved in the quantum regime where the average number of thermally or externally excited magnons and photons is less than one. We also confirm that the coupling strength is proportional to the square root of the number of spins. A nonmonotonic temperature dependence of the Kittel-mode linewidth is observed below 1 K and is attributed to the dissipation due to the coupling with a bath of two-level systems.

Resolving quanta of collective spin excitations in a millimeter-sized ferromagnet.

Combining different physical systems in hybrid quantum circuits opens up novel possibilities for quantum technologies. In quantum magnonics, quanta of collective excitation modes in a ferromagnet, called magnons, interact coherently with qubits to access quantum phenomena of magnonics. We use this architecture to probe the quanta of collective spin excitations in a millimeter-sized ferromagnetic crystal. More specifically, we resolve magnon number states through spectroscopic measurements of a superconducting qubit with the hybrid system in the strong dispersive regime. This enables us to detect a change in the magnetic moment of the ferromagnet equivalent to a single spin flipped among more than 1019 spins. Our demonstration highlights the strength of hybrid quantum systems to provide powerful tools for quantum sensing and quantum information processing.

QUANTUM INFORMATION. Coherent coupling between a ferromagnetic magnon and a superconducting qubit.

Rigidity of an ordered phase in condensed matter results in collective excitation modes spatially extending to macroscopic dimensions. A magnon is a quantum of such collective excitation modes in ordered spin systems. Here, we demonstrate the coherent coupling between a single-magnon excitation in a millimeter-sized ferromagnetic sphere and a superconducting qubit, with the interaction mediated by the virtual photon excitation in a microwave cavity. We obtain the coupling strength far exceeding the damping rates, thus bringing the hybrid system into the strong coupling regime. Furthermore, we use a parametric drive to realize a tunable magnon-qubit coupling scheme. Our approach provides a versatile tool for quantum control and measurement of the magnon excitations and may lead to advances in quantum information processing.