Category: Seminar

The so-called second quantum technological revolution is evolving at a rapid pace and promises significant impacts not only on science but also within the industrial sector. Progress in this field critically relies on efficient methods for controlling the quantum properties of systems and their dynamics. In this talk we are going to give two illustrative examples of how, using a key algorithm in modern machine learning, automatic differentiation, we can control the properties of interest of a quantum system. Our initial case study focuses on the control of the tunneling probability of particles in a two-mode system. We show that when the quantum system is coupled to an ancilla, one can learn the optimal ancillary component and the optimal coupling, such that the tunneling probability/time can be controlled. The subsequent example addresses the mitigation of decoherence within a quantum system with noise. Employing a similar methodology, we show how we can learn an ancillary system and its corresponding noise parameters to counteract and diminish the impact of system noise.

Artificial atoms in solids are leading candidates for quantum networks, scalable quantum computing, and sensing, as they combine long-lived spins with mobile and robust photonic qubits. A central goal is to realize photonic platforms that can scale and individually address and control single atoms. Recently, silicon has emerged as a promising host material where artificial atoms with long spin coherence times and emission into the telecommunications band can be controllably created and addressed. This field leverages the maturity of silicon photonics to embed quantum emitters into the world’s most advanced microelectronics and photonics platform. However, a current bottleneck is the naturally weak emission rate of artificial atoms. An open challenge is to enhance this interaction via coupling to an optical cavity. In my talk, I will discuss the integration of silicon color centers in optical cavities and show the enhancement of their brightness when successful cavity-atom coupling is achieved. I will further discuss the prospects for their applications in the context of quantum information processing.

CERN has started its Quantum Technology Initiative in order to investigate the use of quantum technologies in High Energy Physics (HEP). A three-year roadmap and research programme has been defined in collaboration with the HEP and quantum-technology research communities. In this context, initial pilot projects have been set up at CERN in collaboration with other HEP institutes worldwide on Quantum Computing and Quantum Machine Learning in particular. This talk will provide an overview of recent results obtained by the different studies, focusing on current usage of quantum machine learning techniques for HEP use cases and beyond.