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.
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. Read More
Daily Archives: March 9, 2020
Which Industries Will Be Transformed By Blockchain (and the Ensuing Data Glut That Follows)?
For all the attention that the Bitcoin cryptocurrency has garnered over the last decade, its most exciting contribution may be blockchain — the technological breakthrough that made a viable, decentralized currency even possible.
For those who are not familiar, blockchain is the cryptographic breakthrough that enables decentralized electronic databases where all parties can coordinate and trust each other without the presence of a centralized authority.
While blockchain was invented by Satoshi Nakamoto and revealed as part of his seminal paper unveiling Bitcoin, its application goes far beyond Bitcoin or even cryptocurrency. Its potential to allow coordination among decentralized players could revolutionize the enterprise data space from supply chains to lending to mining.
In this article, we focus on the enterprise applications of blockchain. What are company attitudes toward its potential? What kind of enterprise applications are made possible with blockchain? And how much data are they likely to generate? Read More
Computational predictions of protein structures associated with COVID-19
The scientific community has galvanised in response to the recent COVID-19 outbreak, building on decades of basic research characterising this virus family. Labs at the forefront of the outbreak response shared genomes of the virus in open access databases, which enabled researchers to rapidly develop tests for this novel pathogen. Other labs have shared experimentally-determined and computationally-predicted structures of some of the viral proteins, and still others have shared epidemiological data. We hope to contribute to the scientific effort using the latest version of our AlphaFold system by releasing structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19. We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community’s interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics. We’re indebted to the work of many other labs: this work wouldn’t be possible without the efforts of researchers across the globe who have responded to the COVID-19 outbreak with incredible agility. Read More
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