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When quantum computing and AI collide

#artificialintelligence

Machine-learning and quantum computing are two technologies that have incredible potential in their own right. Now researchers are bringing them together. The main goal is to achieve a so-called quantum advantage, where complex algorithms can be calculated significantly faster than with the best classical computer. This would be a game-changer in the field of AI. Such a breakthrough could lead to new drug discoveries, advances in chemistry, as well as better data science, weather predictions and natural-language processing.


TensorFlow Quantum Boosts Quantum Computer Hardware Performance

#artificialintelligence

Google recently released TensorFlow Quantum, a toolset for combining state-of-the-art machine learning techniques with quantum algorithm design. This is an essential step to build tools for developers working on quantum applications. Simultaneously, they have focused on improving quantum computing hardware performance by integrating a set of quantum firmware techniques and building a TensorFlow-based toolset working from the hardware level up – from the bottom of the stack. The fundamental driver for this work is tackling the noise and error in quantum computers. Here's a small overview of the above and how the impact of noise and imperfections (critical challenges) is suppressed in quantum hardware.


Quantum and classical computers handle time differently. What does that mean for AI?

#artificialintelligence

As humans, we take time for granted. We're born into an innate understanding of the passage of events because it's essential to our survival. But AI suffers from no such congenital condition. Robots do not understand the concept of time. State of the art AI systems only understand time as an implicit construct (we program it to output time relevant to a clock) or as an explicit representation of mathematics (we use the time it takes to perform certain calculations to instruct its understanding of the passage of events).


AI automatic tuning delivers step forward in quantum computing

#artificialintelligence

Researchers at Oxford University, in collaboration with DeepMind, University of Basel and Lancaster University, have created a machine learning algorithm that interfaces with a quantum device and'tunes' it faster than human experts, without any human input. They are dubbing it "Minecraft explorer for quantum devices." Classical computers are composed of billions of transistors, which together can perform complex calculations. Small imperfections in these transistors arise during manufacturing, but do not usually affect the operation of the computer. However, in a quantum computer similar imperfections can strongly affect its behavior.


Adiabatic Quantum Optimization Fails to Solve the Knapsack Problem

arXiv.org Artificial Intelligence

In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in computer science, with applications in economics, business, finance, etc. We attempt to solve a number of small knapsack problems whose optimal solutions are known; we find that adiabatic quantum optimization fails to produce solutions corresponding to optimal filling of the knapsack in all problem instances. We compare results obtained on the quantum hardware to the classical simulated annealing algorithm and two solvers employing a hybrid branch-and-bound algorithm. The simulated annealing algorithm also fails to produce the optimal filling of the knapsack, though solutions obtained by simulated and quantum annealing are no more similar to each other than to the correct solution. We discuss potential causes for this observed failure of adiabatic quantum optimization.


#ICML2020 invited talk: Iordanis Kerenidis – "Quantum machine learning : prospects and challenges"

AIHub

The third and final ICML2020 invited talk covered the topic of quantum machine learning (QML) and was given by Iordanis Kerenidis. He took us on a tour of the quantum world, detailing the tools needed for quantum machine learning, some of the first applications, and challenges faced by the field. Iordanis started his talk with a bit of background into quantum computing and why we should be interested in it. He stressed that we should not think of quantum computers as just being a faster processor and providing a blanket speed-up. Crucially, the quantum method is a fundamentally different way of performing computation; it could be much faster for certain tasks, but not all.


The coldest computers in the world

BBC News

Imagine the US is under attack. An enemy aircraft, loaded with warheads, is heading towards the coast, dipping in and out of radar. Fighter jets have been scrambled and there's a frantic effort to pinpoint the target. But the nation's best defence is not an aircraft carrier or a missile system. "Use the quantum computer," yells a general.


The future of computing, a perspective

#artificialintelligence

Could it be possible to offer Einstein's intelligence, the context of Gandhi and the memory of all humanity in a consolidated computer platform? Will we be able, in the near future, to improve man - machine collaboration and connect both? So we are better able that to make more intelligent decisions? All of us have been generating enormous mountains of data for some years, thanks to 6 billion smartphones and around 30 billion connected sensors. To give you an idea; together we are producing 44 Zettabytes of data this year. One Zettabyte is comparable to 700,000 times the largest library in the world times 44.


UVA Uses Machine Learning, Quantum Computing to Study Genetic Disease - Scigazette.com

#artificialintelligence

A team from the University of Virginia School of Medicine is leveraging the power of quantum computing to gain better insight into genetic diseases with machine learning. Although quantum computers are still in their infancy, the researchers noted that when they do advance, they could offer computing power on a scale that's unimaginable on traditional computers. "We developed and implemented a genetic sample classification algorithm that is fundamental to the field of machine learning on a quantum computer in a very natural way using the inherent strengths of quantum computers," said Stefan Bekiranov, PhD. "This is certainly the first published quantum computer study funded by the National Institute of Mental Health and may be the first study using a so-called universal quantum computer funded by the National Institutes of Health." Quantum computers can consider significantly more possibilities than traditional computer programs.


Here's why AI didn't save us from COVID-19

#artificialintelligence

When the COVID-19 pandemic began we were all so full of hope. We assumed our technology would save us from a disease that could be stymied by such modest steps as washing our hands and wearing face masks. We were so sure that artificial intelligence would become our champion in a trial by combat with the coronavirus that we abandoned any pretense of fear the moment the curve appeared to flatten in April and May. We let our guard down. Pundits and experts back in January and February very carefully explained how AI solutions such as contact tracing, predictive modeling, and chemical discovery would lead to a truncated pandemic.