CSIRO researchers have shown how quantum computing can handle large datasets faster and more accurately than traditional computers. The team used quantum machine learning to compress and analyse data without losing important details.
Quantum computers use qubits that can exist in multiple states at once. This allows them to process many possibilities at the same time. Traditional computers work with bits that are either “on” or “off.”
Dr Muhammad Usman, a CSIRO quantum scientist, said the team tested the technology using groundwater monitoring data. “Quantum machine learning can simplify large sets of data while keeping key information,” he said. “This approach can be applied to traffic management, agriculture, healthcare, and energy optimisation.”
The method can help optimise traffic to reduce congestion and emissions. It can also process medical images with higher accuracy for faster diagnosis.
Dr Liming Zhu, Research Director at CSIRO’s Data61, said practical applications are critical for quantum technology. “Our work identifies performance metrics and challenges, helping guide future hardware and software development,” Dr Zhu said. “This brings us closer to real-world demonstrations using quantum computing.”
The research was published in the journal Advanced Science under the title Self-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays. Co-authors include Dr Zeheng Wang, Dr Timothy van der Laan, and Dr Muhammad Usman.
The study highlights the potential for quantum machine learning to transform industrial and real-world data analysis, building on Australia’s decades-long leadership in quantum research.