Currently Active Projects

We are actively seeking PhD and MSc research students. If you are interested in one of the active projects described below, please feel free to contact us via email.

For Scholarships and Admission Information:

Intersection of Quantum Computing and Machine Learning

Quantum computing is an emerging paradigm of computing for computationally intensive problems which are currently intractable on classical computing platforms. It is believed that within the next decade, quantum computing will have disruptive impact in many areas of development including materials design, data science and machine learning, health, climate science, and combinatorial optimisation. Among these, machine learning is widely considered as the recipient of early quantum advantage.

In this project, we are interested to explore the intersection of quantum computing and machine learning. We believe that classical machine learning could help in design and characterisation of quantum processors, in particular learning quantum noise in-situ and performing error mitigation by innovative quantum control. On the other hand, quantum machine learning algorithms can help to develop new methods with superior computational efficiency and/or enhanced accuracy and robustness.

npj Computational Materials 6, 19, 2020

arXiv: 2110.05854, 2021

Quantum Device Characterisation, Benchmarking and Applications

The development of a large-scale fault-tolerant quantum computer may still require significant technological advancement, however, near-term quantum devices consisting of 50-100 qubits are already accessible through cloud-based services. Within the next couple of years, the anticipation is for up to >1000 qubit devices. These quantum devices offer an excellent platform to design, implement and benchmark quantum simulations/algorithms relevant for real-world applications such as in the areas of data science, finance, chemistry, traffic routing, and optimisation.

In this project, our research has been focused on developing new quantum error correction schemes to improve the fidelity of available quantum devices and test their capabilities for proof-of-concept real-world applications. We have been working on both, implementing conventional error correction codes on IBM Q devices and learning noise variationally and apply classical optimisation techniques to (in situ) improve the fidelities of quantum circuits. The understanding and characterisation of crosstalk and non-Markovian noise is going to play important role in boosting the capabilities of the current generation of quantum devices. Our team is also working on the application of advanced machine learning tools to design an autonomous syndrome processor (decoder) for novel surface code schemes which is crucial for large-scale fault-tolerant quantum computing.

arXiv:2110.05854, (2021)

Quantum Hardware Design and Scalability

In this project, we focus on emerging topics of research in quantum technologies namely integrated spin qubit devices, spin-photon interfaces and single photo emitters and detectors. We develop and apply high-end computational methods to tackle key challenges both at the level of fundamental qubit material design as well as at the quantum device level where qubit control, couplings and scale-up protocols are important parameters of interest. Our group has extensive expertise in spin qubit design based on silicon material system which is one of the most widely used material system in the microelectronic industry. Silicon has been a workhorse for computing devices over the last many decades and spin qubits in silicon could leverage from many decades of advancements in fabrication and device design. Solid state spin qubits in silicon are one of the frontrunner candidates for quantum computing devices and related technologies due to the associated very long coherence times (of the order of seconds!) and promising pathways for scalability to large-scale quantum architectures which are crucial to implement error correction.

We are one of the leading groups in Australia and have made significant contributions to advance the Australian national quantum hardware development effort through the ARC Center of Excellence CQC2T platform. Our team has developed a multi-million-atom electronic structure tool which is capable of providing unprecedented understanding of solid-state qubit spin physics. Our work in the last few years includes new proposals for the design of high-fidelity two-qubit quantum gates, the world’s first spatial metrology of spin qubits with single atom precision, spatially resolved mapping of coupled qubit interactions, a framework for autonomous characterisation of qubits by machine learning and a proposal for exchange-based scalable quantum computer architecture. Building upon this extensive experience of working with solid-state spin environment and expertise in design of quantum computing devices, this project will continue the development of new protocols for the implementation of quantum control, spin-photon coupling and scalable quantum computer architectures.

npj Comp. Mat. 6, 19, (2020)

Nature Nanotechnology 11, 763, (2016)

Comp. Materials Science, 193, 110280, (2021)

Quantum sensing by magnetic coupling of a single spin in solid-state environment

Single spins associated with impurity atoms in a solid-state environment such as silicon or NV-diamond system offer a highly promising platform for the implementation of quantum sensing technologies. In this project, we are interested to develop novel quantum sensing and imaging platforms by leveraging the magnetic dipole coupling of isolated impurity spins in silicon for the purpose of nanoscopic imaging of bio-molecular systems at single atom precision level.

The demonstrated scalable fabrication and high precision metrology of impurity spins in silicon is expected to facilitate a molecular imaging capability which can allow high precision structural characterisation at large scales commensurate with many target virus and lipid-membrane systems.

arXiv:2112.03623, (2021)

Metal-Organic Frameworks (Custom-build 2D Materials)

Two-dimensional metal-organic frameworks (2D-MOFs) are highly versatile material system which can be custom-build for the next generation technologies such as organic topological insulators for dissipationless electronics and 2D magnetic materials. One of the key goals of this project is to investigate long-range magnetic ordering in 2D-MOF structures. Elucidating such magnetic properties can be important for understanding possible nontrivial topological electronic properties of these systems, since the latter can result from magnetic phenomena but also be the cause of well-defined spin structure. The presence of such spin structure in 2D materials provides an ideal platform for the realisation of 2D magnets, with applications including in sensing and hard-disk data storage. 2D-MOF structures can be engineered to implement 2D magnets by the selection of metal ions and the selection of structural morphologies which is the subject of our ongoing research.

Due to the availability of a large number of building blocks (molecules and metal atoms) as well as several possibilities to assemble 2D materials by bottom-up approach, the 2D-MOFs form a tremendously large design space which is impractical to explore via direct quantum mechanical simulations based on DFT theory. Alternatively, a machine learning framework, carefully trained by carrying out the rigorous theoretical analysis of a set of selected materials, can rapidly screen a large database of unknown materials to reliably identify those of the same character at only a fraction of the computational cost. In this project, our team is building a machine learning framework which will utilize advanced techniques such as quantitative structure-property relationship (QSPR) to establish a correlation between the electronic and spin properties of 2D-MOFs with their composition, structure, and symmetry. To enable accurate and fast learning, the QSPR analysis will be conducted by employing a variety of advanced algorithms such as linear regression analysis, decision tree regression, and non-linear support vector machines. The detailed insights obtained by QSPR will train an artificial neural network for high-throughput screening and computer-aided design of 2D-MOF TIs.

Small 17, 2005974, (2021)

III-V-Bi-N Materials and Low-dimensional Nanostructures for Photonic Devices

Designing new materials with engineered band-structure properties is a topic of intense research interest in material science and condensed-matter physics communities. While traditionally Arsenides, Phosphides, and Nitrides have been the focus of research for photonic and optoelectronic devices, recently a new class of materials known as Bismides has emerged as promising medium for the design of devices. Bismides, which are typically formed by replacing a small fraction of As atoms in GaAs or InAs with Bi atoms, offer unique properties at the band-structure level which can be exploited to overcome a number of challenges present in today's devices. For example, Auger loss mechanism that severely degrades the efficiency of today's InP-based devices is expected to be suppressed in Bismide based devices due to crossover between band gap and spin split-off energies. A large tuning of the band gap energy as a function of Bi fraction of alloy offer opportunities for targeting wavelengths in telecommunication and infrared range. Other potential applications for Bismide alloys are in the field of photovoltaics and thermoelectric devices.

We have developed a comprehensive atomistic tight-binding framework to investigate the electronic and optical properties of Bismide alloys and quantum well. Our results have shown that by increasing Bi fraction above 10-11%, band gap energy reduces below spin split-off energy, a proof-of-concept for Auger-loss free photonic devices. Atomistic resolution studies have predicted a crucial role of alloy disorder related effects, with important implications towards understanding device characteristics and designing future devices with tailored functionalities.

Nanoscale 12, 20973, (2020)

Nanoscale, 11, 20133, (2019)

Phys. Rev. Applied 10, 044024, (2018)

Phys. Rev. Materials 2, 044602, (2018)

APL 104, 071103, (2014)

Machine Learning Design of Materials and Devices

Discovery of custom-designed nanomaterials is often an expensive pursuit, which typically relies on multiple trial and error experiments, followed by energy draining manufacturing processes. Even with access to supercomputers, traditional DFT or tight-binding methods takes many CPU hours to execute even a few set of simulations, leaving the exploration of a vast parameter space nearly a formidable task. Contrarily, machine learning approaches could provide reliable, robust, autonomous, and time efficient discovery of new materials at only a fractional computational cost. In this project, our vision is to integrate state-of-the-art multi-million-atom electronic structure and transport calculations with advanced machine learning tools which can form a powerful theoretical framework capable of designing nanomaterials with target functionalities. Our focus is to advance emerging photonic and quantum technologies which are anticipated to drive Industry 4.0 revolution.

Currently Inactive Projects

Self-Assembled Quantum Dots, Quantum Dot Molecules, Quantum Dot Stacks

Self-assembled In(Ga)As/GaAs quantum dots are a promising solid-state system, and are widely employed for the design of a variety of optoelectronic devices and quantum information applications. Based on multi-million-atom simulations, we provide an in-depth understanding of their electronic and optical properties, and perform engineering of related geometry parameters for implementation of devices with tailored functionalities. We have investigated both single quantum dots, as well as large stacks of strongly-coupled quantum dots.

Nanoscale 7, 16516, (2015)

(Rapid Comm.) PRB 89, 081302R, (2014)

Nanotechnology 23, 165202, (2012)

IEEE Trans. Nanotech., 8, 3, (2009)