In recent years, the research focus of our Lab has shifted from the investigation of nanoscale physics at the fundamental atomistic scale to developing quantum algorithms and software for advancing quantum information science. At present, the QL2Q Lab has been primarily working at the forefront of quantum computing, with the aim (1) to reduce the barrier for quantum application developers by offering efficient quantum software solutions with hardware agnostic approach and (2) to advance the usability and adaptation of quantum technologies for real-world applications by developing and benchmarking quantum algorithms. Our team has been developing a full-stack quantum software platform underpinned by a powerful compiler which offers capabilities of quantum error correction/mitigation, optimised quantum control, hardware mapping, etc. Another key focus for our research is at the intersection of quantum computing and machine learning. We are developing quantum machine learning algorithms and investigating their capabilities for quantum enhanced robustness, efficiency and accuracy. One the other hand, we are interested in how classical machine learning could help in advancing quantum computing such as in quantum material design and metrology, noise characterisation and mitigation, and quantum error correction.

Before 2018, the primary goal of the Quanics Lab was to advance Quantum and Photonics Technologies which included nano-devices with custom-designed light-matter interactions (i.e., light generation, detection and conversion), and nano-devices for quantum computing and quantum sensing applications. For this purpose, we investigated a wide range of nano-materials including semiconductors (III-Vs, Si, Ge, SiGe, etc.), emerging 2D materials and metal-organic systems. We also studied low-dimensional nanostructures such as impurities in semiconductors, quantum dots, quantum wells, nanowires, nano-crystals and nano-rods.

Being computational scientists, our research was driven by the development and application of high-end multi-scale computational methods based on DFT, tight-binding (TB) and molecular dynamic (MD) theories. An integral component of our work was the development and application of advanced machine learning tools in materials discovery, as well as in characterisation, control and operational aspects of qubit devices and scalable error-corrected quantum computer architectures. We were also interested in probing condensed-matter and spin physics at the fundamental scale in solid-state environments.