Quantum computation: through the algorithm and complexity theory lens. This project aims to advance our knowledge of quantum computation through the lens of algorithm and complexity theory. Three core areas of the theory will be examined: interactive computing models, query complexity, and circuit lower bounds. The expected outcomes include: revealing the quantum advantages of interactive computing models; techniques for verifying quantum devices in the cloud and quantum cloud computing in gener ....Quantum computation: through the algorithm and complexity theory lens. This project aims to advance our knowledge of quantum computation through the lens of algorithm and complexity theory. Three core areas of the theory will be examined: interactive computing models, query complexity, and circuit lower bounds. The expected outcomes include: revealing the quantum advantages of interactive computing models; techniques for verifying quantum devices in the cloud and quantum cloud computing in general; sharpening the separation between algorithm performance in quantum and classical query models; establishing both unconditional and conditional hardness results for quantum circuits. This comprehensive understanding will enhance Australia's research portfolio in the theory of quantum computing.Read moreRead less
Supra-classical quantum simulation in physically restricted models of quantum computation. Quantum computation evolved from the revolutionary twentieth century theories of Quantum Mechanics and Computer Science, offering computational power that potentially transcends traditional computing models. This project will accelerate the delivery of the promised benefits of quantum computation through advancing the theory of quantum simulation.
Quantum algorithms for quantum chemistry. This project aims to develop more efficient algorithms to simulate quantum chemistry on quantum computers. Quantum computers have the potential to perform calculations that would be intractable for even the largest supercomputers, but need to be programmed in a radically different way to achieve this speed. One of the most important applications of quantum computers is to simulate quantum mechanics to predict the properties of molecules and materials, an ....Quantum algorithms for quantum chemistry. This project aims to develop more efficient algorithms to simulate quantum chemistry on quantum computers. Quantum computers have the potential to perform calculations that would be intractable for even the largest supercomputers, but need to be programmed in a radically different way to achieve this speed. One of the most important applications of quantum computers is to simulate quantum mechanics to predict the properties of molecules and materials, and thereby design them. Current quantum algorithms are very resource intensive, making them impractical for the foreseeable future. The expected outcome of this project is to provide much more efficient algorithms that can be run on quantum processors in the near future.Read moreRead less
Quantum algorithms for computational physics. The project intends to provide a solid base of quantum algorithms that would enable quantum computers to tackle currently insurmountable problems. Many of the highest-value applications in computing are based on solving problems in physics. Quantum computers take advantage of the power of quantum mechanics to outperform even the fastest conceivable supercomputers. This project plans to use new tools in quantum algorithms to provide much faster ways f ....Quantum algorithms for computational physics. The project intends to provide a solid base of quantum algorithms that would enable quantum computers to tackle currently insurmountable problems. Many of the highest-value applications in computing are based on solving problems in physics. Quantum computers take advantage of the power of quantum mechanics to outperform even the fastest conceivable supercomputers. This project plans to use new tools in quantum algorithms to provide much faster ways for quantum computers to simulate physics, including molecular modelling, field theories that explain elementary forces in the universe, and differential equations needed to model classical physics. The increases in computing speed have the potential to enable new technology in areas such as drug design and materials science, as well as providing testable predictions for new theories of physics.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100156
Funder
Australian Research Council
Funding Amount
$329,446.00
Summary
Quantum property testing-a fundamental technology for quantum big data. This project aims to develop fundamental technology for analysing the big data that arises from quantum physics and is expected to generate new knowledge in the fields of quantum information and theoretical computer science. New algorithms for verifying entanglement and estimating entropy of quantum systems are anticipated. These outcomes should significantly enhance our ability to learn information from, and about, quantum ....Quantum property testing-a fundamental technology for quantum big data. This project aims to develop fundamental technology for analysing the big data that arises from quantum physics and is expected to generate new knowledge in the fields of quantum information and theoretical computer science. New algorithms for verifying entanglement and estimating entropy of quantum systems are anticipated. These outcomes should significantly enhance our ability to learn information from, and about, quantum systems with immediate applications in the engineering of quantum technologies, especially for current experiments.Read moreRead less
Approximate algorithms and architectures for area efficient system design. This project aims to develop simpler but reliable image recognition systems that can run on low-cost, small-scale platforms, for use in driver monitoring system (DMS) applications. Cheaper reliable DMS will lead to wider availability of this technology to end users and improve safety of motor vehicles. This project will develop approximate algorithmic and circuit techniques, provide training for research students and buil ....Approximate algorithms and architectures for area efficient system design. This project aims to develop simpler but reliable image recognition systems that can run on low-cost, small-scale platforms, for use in driver monitoring system (DMS) applications. Cheaper reliable DMS will lead to wider availability of this technology to end users and improve safety of motor vehicles. This project will develop approximate algorithmic and circuit techniques, provide training for research students and build capability in the area of approximate computing. It is also expected to lead to commercial products, licences and revenue, which will enable new job creation.
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Visual interaction methods for clustered graphs. This project aims to improve human understanding of huge network data sets, such as those arising in social networks, biological networks, and very large software structures. The project will enable analysts to explore and interact with such data sets, leading to better understanding.
Algorithms and data structures to support automated analysis of trajectory data. The emergence of a variety of tracking devices, surveillance systems and even electronic transaction and phone networks has resulted in the production of large amounts of positional information for vehicles, people and animals. The aim of the project is to develop tools that support automated analysis of such data sets.
Local reoptimization for turbocharging heuristics. Theoretical computer science has up until now had little impact on the design of effective heuristics. While data sets may be large, significant structure is almost always present and important to take into account when designing algorithms. Parameterised complexity considers the underlying structure by parameterising not only on the size of the input but also on structural parameters. This project aims to take advantage of the many opportunitie ....Local reoptimization for turbocharging heuristics. Theoretical computer science has up until now had little impact on the design of effective heuristics. While data sets may be large, significant structure is almost always present and important to take into account when designing algorithms. Parameterised complexity considers the underlying structure by parameterising not only on the size of the input but also on structural parameters. This project aims to take advantage of the many opportunities for new theories in the design of new heuristics and in turbocharging existing heuristics for computationally hard problems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101664
Funder
Australian Research Council
Funding Amount
$357,084.00
Summary
Universal solution for scheduling problems. The aim of this project is to design efficient algorithms that compute universal solutions for scheduling on an unreliable machine. Such solutions are specially suitable for situations where machines can behave unpredictably, such as scheduling in cloud computing.