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Tuesday, March 8, 2016

Artificial Intelligence (AI) Used To Design Quantum Mechanics Experiments

Quantum Mechanics Is So Weird That Scientists Need AI to Design Experiments

Researchers at the University of Vienna have created an algorithm that helps plan experiments in this mind-boggling field.

Michelle Starr | March 6, 2016

Quantum mechanics is one of the weirdest fields in science. Even physicists find it tough to wrap their heads around it. As Michael Merrifeld of the University of Nottingham says, "If it doesn't confuse you, that really just tells you that you haven't understood it."
This makes designing experiments very tricky. However, these experiments are vital if we want to develop quantum computing and cryptography. So a team of researchers decided, since the human mind has such a hard time with quantum science, that maybe a "brain" without human preconceptions would be better at designing the experiments.

The fastest quantum random number generator to date. Source:

<more at; related articles and links: (Quantum experiments designed by machines. February 22, 2016) and (Automated Search for new Quantum Experiments. Mario Krenn, Mehul Malik, Robert Fickler, Radek Lapkiewicz, and Anton Zeilinger. Phys. Rev. Lett. 116, 090405 – Published 4 March 2016. [Abstract: Quantum mechanics predicts a number of, at first sight, counterintuitive phenomena. It therefore remains a question whether our intuition is the best way to find new experiments. Here, we report the development of the computer algorithm Melvin which is able to find new experimental implementations for the creation and manipulation of complex quantum states. Indeed, the discovered experiments extensively use unfamiliar and asymmetric techniques which are challenging to understand intuitively. The results range from the first implementation of a high-dimensional Greenberger-Horne-Zeilinger state, to a vast variety of experiments for asymmetrically entangled quantum states—a feature that can only exist when both the number of involved parties and dimensions is larger than 2. Additionally, new types of high-dimensional transformations are found that perform cyclic operations. Melvin autonomously learns from solutions for simpler systems, which significantly speeds up the discovery rate of more complex experiments. The ability to automate the design of a quantum experiment can be applied to many quantum systems and allows the physical realization of quantum states previously thought of only on paper.])>

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