Google Brings Machine Learning, Quantum Computing Initiatives Together in TensorFlow Quantum
- By John K. Waters
Google's new software framework for quantum machine learning, TensorFlow Quantum (TFQ), unveiled last week, was developed to provide "the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms that could potentially yield a quantum advantage," the company says.
TFQ is an open source quantum machine learning library for rapid prototyping of hybrid quantum/classical machine learning models. It provides the high-level abstractions needed for the design and implementation of both discriminative and generative quantum-classical models by providing quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators.
Developed by Google's X unit, the Institute for Quantum Computing at the University of Waterloo, NASA's Quantum AI Lab, Volkswagen and Google Research, TFQ uses the TensorFlow machine learning development platform to integrate quantum computing algorithms and logic designed in the Cirq open source quantum circuit library.
"TFQ allows researchers to construct quantum datasets, quantum models, and classical control parameters as tensors in a single computational graph," the Google Research team explained in a blog post. "The outcome of quantum measurements, leading to classical probabilistic events, is obtained by TensorFlow Ops. Training can be done using standard Keras functions."
The team behind TFQ explained their thinking and their goals in a whitepaper, "TensorFlow Quantum: A Software Framework for Quantum Machine Learning."
"TensorFlow Quantum is intended to accelerate the development of quantum machine learning algorithms for a wide array of applications," they wrote. "We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage."
Google researchers claimed to have reached a major milestone in the evolution of quantum computing called "quantum supremacy" last year. This unofficial designation is earned by computing devices that can solve problems no classical computer can handle.
"Quantum machine learning is a very new and exciting field, so we expect the framework to change with the needs of the research community, and the availability of new quantum hardware," they added.
John K. Waters is the editor in chief of a number of Converge360.com sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS. He can be reached at firstname.lastname@example.org.