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.

Google's quantum supremacy experiment used 53 noisy qubits to demonstrate it could perform a calculation in 200 seconds on a quantum computer that would take 10,000 years on the largest classical computer using existing algorithms. This marks the beginning of the Noisy Intermediate-Scale Quantum (NISQ) computing era. In the coming years, quantum devices with tens-to-hundreds of noisy qubits are expected to become a reality. Quantum computing relies on properties of quantum mechanics to compute problems that would be out of reach for classical computers. A quantum computer uses qubits.

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.

Honeywell, a company best known for making control systems for homes, businesses and planes, claims to have built the most powerful quantum computer ever. Other researchers are sceptical about its power, but for the company it is a step toward integrating quantum computing into its everyday operations. Honeywell measured its computer's capabilities using a metric invented by IBM called quantum volume. It takes into account the number of quantum bits – or qubits – the computer has, their error rate, how long the system can spend calculating before the qubits stop working and a few other key properties. Measuring quantum volume involves running about 220 different algorithms on the computer, says Tony Uttley, the president of Honeywell Quantum Solutions.