Quantum and Photonic Science 

Quanics Lab

Over the years, the research focus at the Quanics Lab has evolved from the investigation of nanoscale physics at the fundamental atomistic scale to more recently developing quantum algorithms and software for advancing quantum information science.

2018 Onwards, the Lab has been 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 increase the usability of quantum technologies for real-world applications by developing and benchmarking quantum algorithms. Our team is 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. We are also developing quantum machine learning algorithms and investigating their capabilities for quantum enhanced robustness, efficiency and accuracy. We are particularly interested at the intersection of quantum computing and machine learning on both fronts, how classical machine learning help advance quantum computing and how quantum computing can enhance the performance of machine learning.

Before 2018, the primary goal of the Quanics Lab was to advance Quantum and Photonics Technologies which include 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.

Broad Areas of Interest:

Computational Nano-electronics

Quantum Information Science & Technology

Applied Machine Learning

High Performance Computing

Materials Science and Engineering

Enabling Technologies:

Quantum Computing (Materials, Devices, Software, Simulations, Algorithms)

Quantum Sensing

Photonic & Electronic Devices for Industry 4.0

Quantum Security (Data & Communications)

Big Data 

Computational Resources:

Simulations with realistic dimensions of nano-electronic and quantum devices ranging from 10-100 nm include several hundred thousands to a few million atoms in the simulation domain and therefore require high-performance super-computing machines. Our work is supported by computational resources provided by the following super-computers: