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Quantum Generative Diffusion Models for Molecular Research. This project will devise quantum generative diffusion models to equip classical counterparts with the ability to harness quantum data that naturally arise in molecular research. Theoretical foundations for analysing fast sampling methods with the help of inductive bias regarding the input data and employed circuits will validate efficient quantum generative diffusion models that have training and sampling advantages over classical count ....Quantum Generative Diffusion Models for Molecular Research. This project will devise quantum generative diffusion models to equip classical counterparts with the ability to harness quantum data that naturally arise in molecular research. Theoretical foundations for analysing fast sampling methods with the help of inductive bias regarding the input data and employed circuits will validate efficient quantum generative diffusion models that have training and sampling advantages over classical counterparts. Outcomes include applications in molecular conformation generation, compound screening, and drug design. The innovative research will significantly benefit Australia’s science, industry and health, and will maintain Australia’s global leading role in quantum machine learning and molecular research.Read moreRead less
Theoretical Foundations of Ethical Machine Learning. The project aims to develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project aims to develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification ....Theoretical Foundations of Ethical Machine Learning. The project aims to develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project aims to develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of the inevitable trade-offs between fairness and utility. The benefits of the project should include better ways of managing these trade-offs, a competitive advantage for Australian firms developing the technology, and will ensure that the country retains a social license to use the technology.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL200100176
Funder
Australian Research Council
Funding Amount
$3,128,080.00
Summary
Theoretical Foundations of Ethical Machine Learning. The project will develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project will develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of t ....Theoretical Foundations of Ethical Machine Learning. The project will develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project will develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of the inevitable trade-offs between fairness and utility. The benefits of the project will include the best possible ways of managing these trade-offs, competitive advantage for Australian firms developing the technology, and will ensure that the country retains a social license to use the technology.Read moreRead less