Quantum Computational Intelligence

Imagine solving mathematical problems where you could use the full physical range of computational possibilities within the laws of the universe, and be inspired by the sublime algorithmic intelligence of the human brain. This is precisely why the emerging field of quantum machine learning (QML) has received so much recent attention. In this blog post, we'd like to discuss the fundamental ideas and applied value of machine learning to computation in general, and then contextualize these ideas in a new way within the paradigm of quantum computation. Machine learning – a subfield of computer science related to computational statistics and pattern recognition – emerged in its modern incarnation in the mid-late 20th century as researchers attempted to build thinking machines. While first-generation artificial intelligence took inspiration from the computers of the 1980s to reason about intelligence and view humans like deterministic, syntactical machines, contemporary artificial intelligence instead chooses to build machines that have the adaptability and variability of human in "coping" with the ill-defined problem of being an individual with incomplete information in a complex world.