103 citations
- The University of MelbourneAU18 papers
- Commonwealth Scientific and Industrial Research OrganisationAU16 papers
- Data61AU15 papers
- RMIT UniversityAU13 papers
- Monash UniversityAU11 papers
- Swinburne University of TechnologyAU7 papers
- UNSW SydneyAU5 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- The University of AdelaideAU3 papers
- University of ManchesterGB3 papers
- Australian Research CouncilAU2 papers
- Centre for Quantum Computation and Communication TechnologyAU2 papers
9 papers · 1 filter
Soft x-ray ptychography with SOPHIE: Guide and instrumentation
Tim A. Butcher, Simone Finizio, Lars Heller +7
Soft x-ray ptychography is becoming a key synchrotron microscopy technique in the fields of condensed matter physics, chemistry, and environmental and life sciences. Its attractive…
Almost fault-tolerant quantum machine learning with drastic overhead reduction
Haiyue Kang, Younghun Kim, Eromanga Adermann +2
Errors in the current generation of quantum processors pose a significant challenge towards practical-scale implementations of quantum machine learning (QML) as they lead to traina…
Fully convolutional 3D neural network decoders for surface codes with syndrome circuit noise
Spiro Gicev, Lloyd C. L. Hollenberg, Muhammad Usman
Artificial Neural Networks (ANNs) are a promising approach to the decoding problem of Quantum Error Correction (QEC), but have observed consistent difficulty when generalising perf…
Context-Aware Unit Testing for Quantum Subroutines
Mykhailo Klymenko, Thong Hoang, Samuel A. Wilkinson +6
Software testing is a critical component of the classical software development lifecycle, and this principle is expected to hold true for quantum software as it evolves toward larg…
Variational Quantum Machine Learning with Quantum Error Detection
Eromanga Adermann, Hajime Suzuki, Muhammad Usman
Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterpar…
Reinforcement learning for robust dynamic metabolic control
Sebastián Espinel-Ríos, River Walser, Dongda Zhang
Dynamic metabolic control allows key metabolic fluxes to be modulated in real time, enhancing bioprocess flexibility and expanding available optimization degrees of freedom. This i…