SQC Wins A$3.6M for Quantum Energy Forecasting

Quantum computing is looking for practical use cases beyond the laboratory. Energy forecasting is one of the cleaner tests, because power grids are becoming harder to predict as solar, batteries, EVs and flexible demand all grow at once.

What happened

Silicon Quantum Computing and Schneider Electric progressed to Stage 2 of Australia’s Critical Technologies Challenge Program, receiving A$3.6 million to continue work on hybrid quantum-classical energy forecasting with UNSW Sydney.

The project is focused on forecasting distributed-energy demand, where conventional models can struggle with messy, decentralised power patterns. The team has previously reported an average 20% accuracy improvement, though that still needs to translate into real-world grid operations at scale.

Why it matters

This is a practical quantum signal rather than a vague future-computing claim. Better forecasting could help utilities manage solar output, battery charging, EV demand and building loads more efficiently.

The bigger picture

As AI infrastructure increases electricity demand, the grid needs better prediction tools on both the supply and demand side. Quantum-classical systems may find their first commercial relevance in narrow optimisation problems before broader quantum advantage arrives.