Australian Laureate Fellowships - Grant ID: FL190100167
Funder
Australian Research Council
Funding Amount
$2,895,366.00
Summary
The CMOS Quantum Processor: A path to scalable quantum computing. The project aims to develop a quantum computer processor based on a new technology developed by Professor Dzurak in 2014-15. Remarkably, the qubits, or processing elements, utilise the silicon metal-oxide semiconductor field-effect transistors that constitute today’s microprocessor chips, so existing production plants can be used to fast-track development. The project will realise proof-of-principle systems with 10-20 qubits, to r ....The CMOS Quantum Processor: A path to scalable quantum computing. The project aims to develop a quantum computer processor based on a new technology developed by Professor Dzurak in 2014-15. Remarkably, the qubits, or processing elements, utilise the silicon metal-oxide semiconductor field-effect transistors that constitute today’s microprocessor chips, so existing production plants can be used to fast-track development. The project will realise proof-of-principle systems with 10-20 qubits, to resolve critical issues related to readout, error correction, and long-distance on-chip coupling, to take the technology to a commercial-ready stage. Quantum computing is one of the great scientific challenges of this century, with important applications in pharmaceutical design, finance and national security.Read moreRead less
Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This ....Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This project will benefit robotics, control engineering, infrastructure automation, and other fields that demand the capability to model physical systems from limited data. It will also improve cybersecurity by making learning algorithms resilient to deliberate attacks with false data.Read moreRead less