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Swinburne’s Nexus aims to lead Australia’s quantum economy

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October 13, 2026 by Ivy Delaney

Swinburne University of Technology’s Nano Lab reports combining techniques for both 2D and 3D fabrication and modification of nanoparticles. This capability underpins the Swinburne Quantum Nexus, a new initiative aligned with Australia’s National Quantum Strategy and supported by Quantum Australia. The Nexus aims to strengthen national quantum capability and build global partnerships, positioning Swinburne and Australia as leaders in the emerging quantum economy. The university states that Swinburne Quantum Nexus is a catalyst for discovery, translation and impact, supporting cross-disciplinary collaboration across computing, engineering, physics and industry to address real-world challenges.

Nexus Drives National Quantum Strategy Alignment

This strategic alignment positions Swinburne University of Technology and the nation as potential leaders in the emerging quantum economy, moving beyond fundamental research towards practical applications. The Nexus facilitates growth by strengthening industry ties, building new partnerships and identifying funding opportunities to accelerate quantum technology development. Research at Swinburne is cross-disciplinary, uniting expertise across computing, engineering, physics and industry-focused areas to tackle real-world problems with quantum-enabled solutions.

This collaborative environment supports innovation by bringing diverse perspectives to bear on complex challenges, ensuring research translates into tangible benefits. For example, a current study with Siemens investigates how quantum-enhanced timing can improve the stability and resilience of future power grids, a critical step towards a more secure and efficient energy infrastructure.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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