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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 to Stage 2 of Australia’s Critical Technologies Challenge Program. The companies report that their Stage 1 model improved next-day forecasting accuracy by an average of 20% against a classical benchmark; the expanded work will model hundreds of homes and integrate SQC’s Watermelon chip into Schneider Electric’s AI workflows.
Silicon Quantum Computing (SQC) and Schneider Electric have advanced to Stage 2 of Australia’s Critical Technologies Challenge Program, receiving A$3.6 million to expand energy-forecasting work with UNSW Sydney. The project will test SQC’s Watermelon quantum-enhanced AI chip across hundreds of Australian homes and integrate it into Schneider Electric’s AI workflows, building on results the companies reported from their first stage.
During Stage 1, the joint team applied Watermelon to next-day energy forecasting data covering a 12-month period. SQC and Schneider Electric said the resulting forecasts achieved an average 20% improvement in accuracy compared with a classical benchmark, with gains reaching as high as 41%. The companies’ announcement does not provide further detail on the benchmark’s design or specify how the reported accuracy changes were calculated.
Watermelon produces what SQC describes as quantum features, which are used alongside conventional, or classical, features in predictive models. The Stage 2 project will extend the modelling to hundreds of homes across Australia and connect the technology directly to Schneider Electric’s AI workflows. The funding is from the Australian Government’s Critical Technologies Challenge Program, and UNSW Sydney is a partner in the work.
The companies say better forecasts could help manage distributed energy resources, including rooftop solar, home batteries and electric vehicles. More accurate predictions may support decisions about when to use or store power and how to balance energy supply. However, the reported Stage 1 accuracy results do not by themselves establish that the approach has reduced consumer bills or improved grid performance in live operations.
Testing Forecasts Across Australian Homes
Energy providers and technology firms must make decisions as household energy supplies and demand become less predictable. Rooftop solar changes how much electricity homes draw from the grid; batteries can shift demand across the day; and electric vehicles add charging needs that may vary by household and time. Forecasting accuracy can affect how effectively these resources are coordinated.
If the Stage 1 result holds up in wider testing, quantum-generated features could offer a way to improve an existing forecasting system without replacing conventional computing. SQC’s approach is to combine those features with classical ones, rather than use a quantum processor as a standalone substitute for standard AI infrastructure. Stage 2’s larger sample and workflow integration are intended to test whether the reported gains can carry into a more practical setting.
The potential benefits described by the companies—better use of renewable energy, more effective management of distributed resources and lower costs—remain prospective. The announced funding marks a step toward broader evaluation, not evidence that those outcomes have already been delivered at scale.
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From Stage One to Wider Trials
The Critical Technologies Challenge Program is an Australian Government initiative launched in 2025. SQC and Schneider Electric’s Stage 1 work examined whether Watermelon could improve Schneider Electric’s existing forecasting models. The companies now say the project has passed into Stage 2 with A$3.6 million in funding.
SQC describes Watermelon as an atomically engineered, quantum-enhanced AI chip. In the project, it is used to generate additional features for predictive models, which are then combined with conventional data features. The company says Watermelon is available through cloud access and hardware sales, including turnkey data-centre deployment, and is used by customers in sectors such as telecommunications and banking. These are company-provided descriptions; the announcement does not supply independent evaluations of those deployments.
Schneider Electric develops energy management and automation technologies, including forecasting and optimisation tools for distributed energy resources. Its involvement gives the project a setting in which to test the chip’s output alongside established AI workflows, while UNSW Sydney is also part of the partnership. The announcement does not describe the university’s specific responsibilities.
“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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How the Reported Gains Were Measured
The announcement does not explain the classical benchmark used for comparison, provide the underlying forecast data, or define the metric behind “accuracy.” It also does not say whether the reported average improvement reflects particular homes, time periods or operating conditions. The 41% figure is described as a maximum gain; the source does not state how often that level was reached.
It remains unclear how Stage 2 will measure performance across hundreds of homes, whether the Stage 1 gains will persist in that larger sample, or how the system will perform when integrated into Schneider Electric’s workflows. The announcement gives no schedule, deployment locations, consumer-cost results or independent assessment. Commercial and grid-level benefits have not been established by the information provided.
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Stage Two Expands the Trial
The announced next step is to expand the modelling to hundreds of Australian homes and integrate Watermelon directly into Schneider Electric’s AI workflows. The project is being carried out with UNSW Sydney and supported by the A$3.6 million Stage 2 award. The companies have not announced a timetable for completing the work or publishing results.
Readers will need further reporting on the evaluation method, performance across the expanded sample and whether the forecasts produce measurable improvements in energy management. Any claims about lower household costs or greater renewable-energy use will require outcome data, rather than forecast-accuracy figures alone.
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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 did the companies report from Stage 1?
SQC and Schneider Electric said Watermelon improved next-day forecasting accuracy by an average of 20% against a classical benchmark, with gains of up to 41%, using forecasting data over 12 months. The announcement does not detail the benchmark or calculation method.
What will Stage 2 test?
The project plans to extend modelling to hundreds of homes across Australia and integrate Watermelon into Schneider Electric’s AI workflows.
Have the companies shown that the project lowers energy bills?
No consumer-cost savings are reported in the announcement. Lower costs and more efficient energy use are described as potential benefits, not measured Stage 1 outcomes.
What remains unknown about the results?
The announcement does not specify the accuracy metric, benchmark design, Stage 2 schedule or how performance will be assessed across the larger group of homes. It also provides no independent validation or live grid-outcome data.
Source: rss
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