Researchers enhance quantum machine learning algorithms

Researchers enhance quantum machine learning algorithms Illustration of a Restricted Boltzmann Machine (RBM) bipartite graph where viviv_i are visible nodes, hjhjh_j are hidden nodes and wijwijw_{ij} are the weights connecting the hidden and visible nodes.

Researchers at Florida State University found a way to automatically infer parameters used in an important quantum Boltzmann machine algorithm for machine learning applications.

The work could help build artificial neural networks that could be used for training computers to solve complicated, interconnected problems like image recognition, drug discovery and the creation of new materials.

One way to build neural networks is by using a restricted Boltzmann machine, an algorithm that uses probability to learn based on inputs given to the network. The team found a way to automatically calculate an important parameter associated with effective temperature that is used in that algorithm. Restricted Boltzmann machines typically guess at that parameter instead, which requires testing to confirm and can change whenever the computer is asked to investigate a new problem.(

Their work has been published in Scientific Reports.

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