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Silicon Quantum Computing and Schneider Electric have received A$3.6 million to advance their energy forecasting project with UNSW Sydney in Stage 2 of Australia’s Critical Technologies Challenge Program. The companies say their Stage 1 work improved next-day forecast accuracy by an average of 20% against a classical benchmark; the next phase will test the approach across hundreds of homes and integrate it into Schneider Electric’s AI workflows.
Silicon Quantum Computing (SQC) and Schneider Electric have received A$3.6 million to expand a quantum-enhanced energy forecasting project with UNSW Sydney, after progressing to Stage 2 of Australia’s Critical Technologies Challenge Program. The next phase will extend modelling to hundreds of Australian homes and connect SQC’s Watermelon chip directly to Schneider Electric’s AI workflows.
In the companies’ account of Stage 1, their team applied SQC’s Watermelon quantum-enhanced AI chip to next-day energy forecasting data covering a 12-month period. They reported an average 20% improvement in forecasting accuracy compared with a classical benchmark, with gains reaching up to 41%. The announcement does not provide the benchmark’s underlying accuracy scores or a detailed explanation of how the improvement was calculated.
Watermelon produces what SQC describes as quantum features, which are combined with conventional data features in a forecasting model. Stage 2 funding is intended to broaden the modelling beyond the initial work and test the approach across more homes. The partners also plan to integrate Watermelon into Schneider Electric’s AI workflows, a step that could help show whether the system can be used as part of an operational forecasting process.
The funding is from the Australian Government’s Critical Technologies Challenge Program. The announcement identifies UNSW Sydney as a project partner, but does not specify how the A$3.6 million will be divided among the participants or provide a project completion date.
Forecasting for More Distributed Energy
Electricity systems must account for growing numbers of household resources, including rooftop solar, home batteries and electric vehicles. Their availability and use can change the timing and volume of electricity flowing through local networks. Better forecasts could help energy operators plan around those changes and manage distributed resources more effectively.
The reported Stage 1 gains are a company-reported result, not a published demonstration of system-wide cost or emissions reductions. If the improvements hold up at a larger scale, more accurate predictions could support renewable energy use and help limit unnecessary costs for consumers. Whether those benefits follow depends on how the models perform across varied households and whether the technology can be integrated reliably into existing systems.
The project also offers a practical test of a hybrid computing approach: using a quantum processor to generate features alongside conventional computing rather than replacing classical systems. Stage 2 matters because it moves the work toward larger-scale testing and workflow integration, beyond the initial forecasting analysis. It does not, by itself, establish that quantum-enhanced forecasting is ready for broad commercial deployment.
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From Stage One to Broader Trials
The project focuses on predicting household energy needs and conditions relevant to managing distributed energy resources. Schneider Electric develops energy management and forecasting technologies, while SQC is developing quantum computing hardware and related systems. The partners worked with UNSW Sydney during the program’s first stage.
According to the announcement, Stage 1 examined whether Watermelon could improve Schneider Electric’s forecasting models. The reported test used next-day forecasts over 12 months and compared results with a classical benchmark. The companies say the chip supplies additional features to a model that still uses conventional features; they do not describe the work as a complete quantum-computing replacement for established forecasting methods.
SQC says Watermelon, launched in 2025, is available through cloud access or hardware sales, including turnkey data-center deployment, and is used with customers in sectors such as telecommunications and banking. Those are company-provided deployment details; the announcement does not identify the customers or describe their results. The new government funding specifically supports the energy forecasting project.
“We have always believed that quantum processors would work alongside CPUs and GPUs to deliver real-world performance gains.”
— Michelle Simmons, founder and CEO of Silicon Quantum Computing
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Performance Beyond the Initial Benchmark
The announcement does not give the classical benchmark’s baseline accuracy, the forecast error metric used, or the number and characteristics of homes in the Stage 1 dataset. It also does not explain whether the reported 20% average improvement refers to a relative change or another calculation. The 41% figure is described as a maximum gain, not an average or a result across all households.
It remains unclear how performance will vary across different regions, seasons, household technologies and data conditions. The partners have not announced a trial schedule, a detailed evaluation plan, or a threshold for judging Stage 2 successful. Nor do they report measured changes in consumer bills, renewable energy use or grid operations. Those outcomes should not be inferred from forecasting accuracy alone.
The funding announcement also does not state when the expanded work will finish or when results will be made public. Until more details and independently assessable results are available, the accuracy figures should be understood as claims reported by the project partners, rather than evidence of broad commercial impact.
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Scaling the Forecasting Trial
The next stated step is to expand modelling to hundreds of homes across Australia and integrate Watermelon into Schneider Electric’s AI workflows. That should allow the partners to assess the system in a broader setting than the Stage 1 analysis, although the announcement does not say whether every home will be part of a live operational trial or a modelling exercise.
Readers should look for further information on the homes and data included, the classical comparison method, forecast metrics and results across different conditions. Details about the project timeline and public reporting would also clarify when the performance claims can be evaluated beyond the initial announcement.
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Key Questions
What did SQC and Schneider Electric announce?
They advanced to Stage 2 of Australia’s Critical Technologies Challenge Program and received A$3.6 million to expand their energy forecasting project with UNSW Sydney.
What result did the partners report from Stage 1?
The companies say Watermelon improved next-day forecasting accuracy by an average of 20% against a classical benchmark over a 12-month period, with gains up to 41%. The announcement does not provide the underlying benchmark scores or calculation details.
What will happen in Stage 2?
The project will expand modelling to hundreds of Australian homes and integrate Watermelon directly into Schneider Electric’s AI workflows.
Does the reported accuracy gain prove lower energy bills?
No. The partners describe potential benefits, but the announcement reports forecasting accuracy results, not measured changes in household bills, renewable energy use or grid costs.
When will Stage 2 results be available?
The announcement gives no completion date or publication schedule. It is not yet clear when the expanded modelling will conclude or when its results will be released.
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