quantum


Google's TensorFlow is ready for quantum, but is AI ready for quantum?

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Spoiler alert: Quantum computers may not make your cats and dogs classifiers go any faster. Here's how you can still get a free Windows 10 upgrade You can still use Microsoft's free upgrade tools to install Windows 10 on an old PC running Windows 7 or Windows 8.1. No product key is required, and the digital license says you're activated and ready to go. Google this week announced a new version of its TensorFlow framework for building machine learning models, a kind of mash-up between TensorFlow and Cinq, another framework developed at Google that's designed for building quantum computing algorithms. Together, they could let you build a deep learning model to run on a future quantum computer with no more than a bunch of lines of Python.


Google launches machine learning framework for training quantum models

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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."


TensorFlow Quantum: A Software Framework for Quantum Machine Learning

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Antonio J. Martinez, We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, Hamiltonian learning, and sampling thermal states.


AI Against AI - Blog - Connected World

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What comes to mind when you think of deepfakes? A report by CB Insights got me thinking the other day about deepfakes and their impact on AI (artificial intelligence), quantum, and more. In case you didn't know, deepfakes combine the expressions deep learning and fake and artificial intelligence; and that's what we're talking about with next-gen hack tactics using AI. There are a lot of market numbers about AI-as-a-Service market, AI in financial services, AI in the medical sector, AI in the automotive market, AI in marketing, and AI at the edge. There's just so much to discuss when it comes to AI, and we talk about it relatively frequently in an attempt to try to cover it from all sides.


Quantum Embedding of Knowledge for Reasoning

Neural Information Processing Systems

Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs). These methods embed any given KB into a vector space by exploiting statistical similarities among its entities and predicates but without any guarantee of preserving the underlying logical structure of the KB. This, in turn, results in poor performance of logical reasoning tasks that are solved using such distributional representations. We present a novel approach called Embed2Reason (E2R) that embeds a symbolic KB into a vector space in a logical structure preserving manner. This approach is inspired by the theory of Quantum Logic.


A machine that mimics human sight

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A camera can be compared to the human eye in the sense that both can capture an image. While the camera can only store the image, the nerve network and brain cells that help the human eye see can recognise as well as reconstruct it. The human brain has the power to memorise, recollect and think but even advanced machines cannot think for themselves. Recent breakthroughs in research, however, may just have made possible a machine that can "see". Led by a scientist who calls Calcutta home, researchers at the University of Central Florida (UCF), US, devised a minute gadget that exhibited the ability to recollect and recognise human faces in a way that mimics human brain cells.


These Industrial Robots Get More Adept With Every Task – IAM Network

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Vicarious, a secretive 10-year-old startup backed by Mark Zuckerberg, Elon Musk, and Jeff Bezos, reveals its progress and an initial customer. Google's TensorFlow is ready for quantum, but is AI ready for quantum?


Google launches TensorFlow Quantum, a machine learning framework for training quantum models

#artificialintelligence

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.


Matthias Laux posted on LinkedIn

#artificialintelligence

Just as #aiot brought #ai and #iot together, now we're looking at bringing #artificialintelligence and #quantumcomputing together with Google launching #tensorflow #quantum, joining forces from the respective initiatives at the computer behemoth. As it says in the article, "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." It will be fascinating to watch the progress here for sure. I just hope that we're also making enough progress with #encryption #algorithms as well to avoid artificial intelligent agents equipped with #quantumcomputers bringing our economy to a grinding halt, breaking all secure transmission of data ... https://lnkd.in/deaY8ZD


Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems

arXiv.org Machine Learning

Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis that exploit their unique capabilities to overcome the computational challenges faced by conventional data-driven approaches performed on classical computers. Deep belief networks are integrated into the proposed fault diagnosis model and are used to extract features at different levels for normal and faulty process operations. The QC-based fault diagnosis model uses a quantum computing assisted generative training process followed by discriminative training to address the shortcomings of classical algorithms. To demonstrate its applicability and efficiency, the proposed fault diagnosis method is applied to process monitoring of continuous stirred tank reactor (CSTR) and Tennessee Eastman (TE) process. The proposed QC-based deep learning approach enjoys superior fault detection and diagnosis performance with obtained average fault detection rates of 79.2% and 99.39% for CSTR and TE process, respectively.