Qiskit Machine Learning is now available and includes the computational building blocks that are necessary to bring machine-learning models into the quantum space.
Machine learning is a branch of artificial intelligence that is now widely used in almost every industry. The technology is capable of crunching through ever-larger datasets to identify patterns and relationships, and eventually discover the best way to calculate an answer to a given problem.
Researchers and developers, therefore, want to make sure that the software comes up with the most optimal model possible – which means expanding the amount and improving the quality of the training data that is fed to the machine-learning software. This process inevitably comes with higher costs and much longer training times.
Delegating some parts of the process to a quantum computer could resolve these issues, by speeding up the time it takes to train or evaluate a machine-learning model, but also by vastly increasing what is known as the feature space – the collection of features that are used to characterize the data that is fed to the model, for example "gender" or "age" if the system is being trained to recognize patterns about people. READ MORE
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