Quantum and Photonic Science
Quanics Lab
The primary goal of the Quanics Lab is 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 investigate a wide range of nano-materials including semiconductors (III-Vs, Si, Ge, SiGe, etc.), emerging 2D materials and metal-organic systems. We also study low-dimensional nanostructures such as impurities in semiconductors, quantum dots, quantum wells, nanowires, nano-crystals and nano-rods.
We are computational scientists, and our research is 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 is 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 are 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, Simulations, Algorithms, Applications)
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:
Magnus @ Pawsey Supercomputing Centre through NCMAS Allocation (2016-current)
Raijin @ National Computing Infrastructure through NCMAS Allocation (2016-current)
Spartan @ the University of Melbourne (2014-current)
RCAC @ Purdue University through NCN/Nanohub (2005-15)