Quantum technology holds the promise to create devices that will transform the way we communicate, compute and measure by exploiting the principles of quantum mechanics. This vision is supported by the extraordinary scientific achievements of the past decades in controlling quantum properties of matter, such as the spins of individual electrons and nuclei.
The goal of our research activities is to develop quantum opto-electronic devices based on spins in wide bandgap semiconductors. A spin-based quantum device can be envisioned as an integrated semiconductor chip embedding electronic and photonic components to process and measure quantum states encoded on single spins. Being insensitive to electric noise, spin devices can preserve fragile quantum states over long time. Additionally, a single spin is the smallest possible magnetic field sensor, which can be used to map with nanometric resolution magnetic fields of interest for material science and biology.
We focus on spins associated to optically-active defects in wide-bandgap semiconductors, such diamond and silicon carbide. Silicon carbide, a material widely used in high-power and high-frequency microelectronics, is particularly interesting from this point of view since established techniques for growth, doping and nano-fabrication can be adapted to the development of quantum devices.
In our work, we are trying to address the following scientific questions:
- How can you efficiently measure and control spins in solid-state materials? What determines their properties? How can you preserve their quantum properties despite the perturbations introduced by the environment? How can you scale up your system to include a large number of individually controllable spins?
- How can you integrate spins into opto-electronic devices? How can you efficiently interface spins to photons? How can you make a device with superior performance by exploiting the quantum properties of spins?
- What are the best protocols for spin-based quantum sensing? Can you use adaptive feedback and machine-learning techniques to devise quantum sensing protocols with superior performance?