Google today announced the launch of TensorFlow Quantum, bringing together machine learning and quantum computing initiatives at the company. The framework can construct quantum datasets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators, and train discriminative and generative quantum models. Last fall, Google said it achieved quantum supremacy with the debut of a newly engineered solution. The release of TensorFlow Quantum follows the launch of Azure Quantum and progress by companies like Honeywell. Creating quantum models is made possible with standard Keras functions and by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs, according to a Google AI blog.
Quantum computers have been quite the rage recently with different tech companies vying for the top spot when it comes to building the most powerful quantum machine. While IBM and Google were in the headlines last year for achieving quantum supremacy, other companies like the Industrial giant Honeywell have been quietly working on its own quantum tech. The company plans to make available its quantum machine to clients via the internet in the next three months. However, Honeywell's approach is a little different than the traditional quantum computers which use superconducting qubits to operate. Honeywell's quantum computer uses a different technology, called ion traps, which hold ions in place with electromagnetic fields.
Google has revealed it is bringing together its machine learning and quantum computing initiatives with the launch of TensorFlow Quantum. The machine learning framework has the ability to construct quantum datasets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators and train both discriminative and generative quantum models. According to a Google AI blog, TensorFlow Quantum is able to create quantum models with standard Keras functions and by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs. The release of TensorFlow Quantum comes after Microsoft's launch of Azure Quantum and the recent news that Honeywell is developing a quantum computer with a quantum volume of at least 64 which will be available in the next three months. In an abstract for a paper, authored by members of Alphabet's X unit, The Institute for Quantum Computing at the University of Waterloo, NASA's Quantum AI Lab, Volkswagen and Google Research, submitted to the preprint repository arXiv, the authors explain what they believe TensorFlow Quantum can achieve, saying: "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."
The news: Google is releasing free open-source software that will make it easier to build quantum machine-learning applications. TensorFlow Quantum is an add-on to Google's popular TensorFlow toolkit, which has helped give machine learning a big boost since its launch in 2015. TensorFlow is one of a number of tools that make machine learning more accessible, by simplifying deep neural networks and providing reusable code so that new machine-learning apps don't have to be written from scratch. TensorFlow Quantum is set to do the same for quantum machine learning. TensorFlow Quantum will let you write quantum apps without getting bogged down in the details of the hardware they are running on.
At Xanadu we are developing a photonic quantum computer: a device that processes information stored in quantum states of light. We are very excited by the possibilities that this approach brings. Photonic quantum computers naturally use continuous degrees of freedom -- like the amplitude and phase of light -- to encode information. This continuous, or analog, structure makes photonic devices a natural platform for quantum versions of neural networks. How do we mimic a neural network using a photonic system?