Since 2018, the research focus of the Scalable Quantum Systems (SQS) Lab has largely shifted from the investigation of quantum physics at the fundamental atomistic scale to developing quantum algorithms and scalable quantum software for advancing quantum information science and its real-world applications. At present, the Lab is working at the forefront of quantum computing, with the aims: 

(1) to reduce the barrier for quantum application developers by offering efficient quantum software solutions (software abstraction, compiler, resource optimisation, etc) with hardware agnostic approach 

(2) to advance the usability and adaptation of quantum technologies for practical applications by developing and benchmarking quantum algorithms with noise mitigation and error correction

SQS 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, and hardware mapping. 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 also interested in how classical machine learning could help in advancing quantum computing such as in quantum material design and metrology, quantum noise characterisation and mitigation, and quantum error correction.

Before 2018, the primary goal of the 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 and theoretical 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.