The 8 Ball supercomputer, a spherical monolith that can harness the rules of quantum mechanics to solve vastly complex problems, is not real. It's actually a quantum computer that appeared in a short story written by science fiction novelist Gregory Dale Bear last year. Such computers have yet to escape the realm of science fiction, but recent advances have moved the prospect of a working quantum computer closer to reality. Scientists from Google and the University of Basque Country in Spain believe they have cleared some of the barriers to more complex and useful quantum computers. The technology is based on the idea of quantum bits, or qubits, which loosely correspond to the classic bits stored inside the transistors etched onto silicon.
Google, this week, has launched a new version of their TensorFlow framework -- TensorFlow Quantum (TFQ), which is an open-source library for prototyping quantum machine learning models. Quantum computers aren't mainstream yet; however, when they do arrive, they will need algorithms. So, TFQ will bridge that gap and will make it possible for developers/users to create hybrid AI algorithms combining both traditional and quantum computing techniques. TFQ, a smart amalgamation of TensorFlow and Cinq, will allow users to build deep learning models to run on a future quantum computer with minimal lines of Python. According to the Google AI blog post, TFQ has been designed to provide the necessary tools to bring in the techniques of quantum computing and machine learning research communities together in order to build and control natural and artificial quantum systems.
This blog post is an overview of quantum machine learning written by the author of the paper Bayesian deep learning on a quantum computer. In it, we explore the application of machine learning in the quantum computing space. The authors of this paper hope that the results of the experiment help influence the future development of quantum machine learning. With no shortage of research problems, education programs, and demand for talent, machine learning is one of the hottest topics in technology today. Parallel to the success of learning algorithms, the development of quantum computing hardware has accelerated over the last few years.
A company in California just proved that an exotic and potentially game-changing kind of computer can be used to perform a common form of machine learning. The feat raises hopes that quantum computers, which exploit the logic-defying principles of quantum physics to perform certain types of calculations at ridiculous speeds, could have a big impact on the hottest area of the tech industry: artificial intelligence. Researchers at Rigetti Computing, a company based in Berkeley, California, used one of its prototype quantum chips--a superconducting device housed within an elaborate super-chilled setup--to run what's known as a clustering algorithm. Clustering is a machine-learning technique used to organize data into similar groups. Rigetti is also making the new quantum computer, which can handle 19 quantum bits, or qubits, available through its cloud computing platform, called Forest, today.