A new hybrid system combines classical machine learning techniques with quantum computation to optimize the management of the power grid.

Strategically deployed across the grid, Phasor Measurement Units (PMUs) are sensor devices that measure currents and voltages at a particular time.

PMUs generate huge amounts of data that help utility companies monitor the power grid.

The storage of these large datasets is already a challenge. Accessing and analyzing these datasets is also an issue that is only getting more complicated over time.

In the U.S, the network of PMUs collects a cumulative total of 3 million gigabytes of data every two seconds.

At the moment, many companies use machine learning algorithms to control this data. At the moment, these structures work “fine” because they run on classical computing systems.

However, there are also quantum algorithms which could be much faster and powerful. However, the real potential of these algorithms remains obscured due to our lack of true quantum computers.

But, what if researchers combined the two to accommodate our urgent Big Data analysis needs?

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