Quantum technology has progressed with leaps and bounds in recent times, especially with respect to computing processes. And a Purdue research team led by Sabre Kais who is a professor of chemical physics, is now combining quantum algorithms with classical computing to speed up database accessibility.
More Details About the Latest Quantum Machine Learning Techniques
At the Purdue University, researchers are working on developing algorithms that can work on small-scale quantum computers. The team is using data collected from the U.S. Department of Energy National Labs through their sensors. This data exists in the form of phasor measurement units that collects information on the electrical power grid regarding aspects such as currents, voltages, and power generation. The team needs to continuously monitor the sensors in order to keep the power grid stable, as the values may vary.
According to the team, their method has as an enormous potential that can be applied in various domains. A prime application of the quantum computing processes involves highly efficient optimization of supply-chain and logistics management in industries. By facilitating the quantum computing large-data processes, new chemical and material discoveries can also occur. This can be brought forth by utilizing an artificial neural network called a quantum Boltzmann machine, which can be used for machine learning and data analysis.
As per Alex Pothen, professor of computer science and co-investigator on the project, non-quantum algorithms that are used to analyze gathered data can predict the state of an electric grid. However, as more phasor measurement units are deployed in the electrical network, the need for faster algorithms grows higher. He further continues an explanation about how quantum algorithms for data analysis has the potential to speed up computations, at least from a theoretical sense.
In spite of these breakthroughs, the technology faces numerous challenges. However, these are surely expected to be dealt properly with in the near future.