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Quantum machine learning concepts TensorFlow Quantum

#artificialintelligence

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.


Deep learning-guided surface characterization for autonomous fabrication

AIHub

The semiconductor industry as we know it is facing a critical roadblock that will lead to the end of Moore's law. As transistors continue to shrink, quantum effects have a significant negative consequence on their operation. As such, the development of "beyond CMOS devices" has begun. The push for devices that are cheaper, smaller, and faster has led to the use of scanning probe fabrication. One of the first examples of such a technique was IBM's video "A boy and his atom", where CO molecules were moved along a Cu surface using a sharp metallic tip.


ARTIFICIAL INTELLIGENCE DANGERS TO HUMANITY

#artificialintelligence

China and Big Tech threaten all the worlds people with a Quantum AI Digital Brain on the coming 5G and 6G networks that can form an AI system beyond the control of human beings. "Artificial Intelligence Dangers to Humanity" goes deep into the inter-connections between AI, U.S, China, Big Tech and the worlds use of Facial Recognition, Bio-Metrics, Drones, Smart Phones, Smart Cities, IoT, VR, Mixed Reality, 5G, Robotics, Cybernetics, & Bio-Digital Social Programming. The book is sourced from a 10,000 page report converted to just over 200 pages with pictures and hidden inner meanings. We will cover present, emerging and future threats of Artificial Intelligence with Big Tech, including technology that can be used for assassination or to control humanities ability to have free formed thoughts without being formed by AI Bio-Digital Social Programming. The book will cover Cyborgs, Super Intelligence and how it can form, and in what ways it can travel undetected through The AI Global Network as it connects with the internet and the Human Bio-Digital Network.


K-spin Hamiltonian for quantum-resolvable Markov decision processes

arXiv.org Artificial Intelligence

The Markov decision process is the mathematical formalization underlying the modern field of reinforcement learning when transition and reward functions are unknown. We derive a pseudo-Boolean cost function that is equivalent to a K-spin Hamiltonian representation of the discrete, finite, discounted Markov decision process with infinite horizon. This K-spin Hamiltonian furnishes a starting point from which to solve for an optimal policy using heuristic quantum algorithms such as adiabatic quantum annealing and the quantum approximate optimization algorithm on near-term quantum hardware. In proving that the variational minimization of our Hamiltonian is equivalent to the Bellman optimality condition we establish an interesting analogy with classical field theory. Along with proof-of-concept calculations to corroborate our formulation by simulated and quantum annealing against classical Q-Learning, we analyze the scaling of physical resources required to solve our Hamiltonian on quantum hardware.


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

#artificialintelligence

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

#artificialintelligence

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.