Picking up the pieces ...

How do you turn available components into the right experiment? Credit: TU Wien

Being a lay scientist for a long time, this piece rings true as thought experiments is the art of tinkering, of seeing what fits when trying to solve a problem as creativity is the art of problem solving and ... the size of the problem solved dictates the significance of the creative act. In reading about Einstein, Bohr and all the other giants in physics, one is struck with just how vital the art of seeing truly is. Math is not the vision, it's what comes after in testing whether the thought experiment has legs and is deemed ready to face reality when placed under the microscope of a real world experiment validating whether the thought experiment rings true or false as needs warrant. Think Einstein and the theory of general relativity. Only when Eddington showed that gravity bends light when photographing a total eclipse was Einstein's theory finally accepted

The 29 May 1919 solar eclipse

The Eddington experiment was an observational test of general relativity, organised by the British astronomers Frank Watson Dyson and Arthur Stanley Eddington in 1919. Observations of the total solar eclipse of 29 May 1919 were carried out by two expeditions, one to the West African island of Príncipe, and the other to the Brazilian town of Sobral. The aim of the expeditions was to measure the gravitational deflection of starlight passing near the Sun.[1] The amount of deflection was predicted by Albert Einstein in a 1911 paper; however, his initial prediction proved inaccurate because it was based on an incomplete theory of general relativity. Einstein improved his prediction after finalizing his theory in 1915 and obtaining the solution to his equations by Karl Schwarzschild. Following the return of the expeditions, the results were presented by Eddington to a joint meeting of the Royal Society and Royal Astronomical Society in London[2][3] and, after some deliberation, were accepted. Widespread newspaper coverage of the results led to worldwide fame for Einstein and his theories.

Can AI do the drill?

The best experiment from existing components

This is a typical situation in experimental physics: You have a laboratory full of equipment—perhaps lasers, lenses and mirrors, perhaps different detectors and electronic components. All of these can be combined in an almost incomprehensible number of ways. From this vast range of experimental possibilities, you have to select one that can provide new insights into the universe.

Normally, this requires intuition and a great deal of experience. But sometimes even that is not enough, as Mario Krenn discovered. Today, he is a professor of machine learning in science at the University of Tübingen. As a student in Vienna, he was working on the setup for a quantum experiment. But neither he nor the other members of his research group could find a suitable experimental configuration capable of demonstrating the desired quantum effects.

So Krenn decided to ask the computer. He described the individual components available to him mathematically, then had an algorithm search for combinations of these components that would result in a meaningful experiment.

"Programming it only took a few hours. Then I went home and left the computer running," Krenn says. "When I came into the office the next day, the program had produced a file containing a proposed solution. Of course, that was extremely exciting. I immediately started analyzing the proposal, and indeed: Unlike all of us, the computer had found an experimental setup that satisfied the necessary criteria."

Ballooning

"It is an enormous optimization problem," Krenn says. "There is an overwhelmingly large space of possible experiments that can be built from the available components. The computer has to search this space systematically in order to find the best possible solution."

"The results are impressive," says Philipp Haslinger, head of the Center for Electron Microscopy at TU Wien. "In electron microscopy in particular, we are only now beginning to work systematically with entanglement and new quantum-mechanical microscopy concepts. Human intuition in this area is often still very limited. Artificial intelligence can therefore identify microscope designs that a human would probably never have come up with, but which can produce significantly better images or offer entirely new measurement possibilities."

This makes sense as properly setting up a complex experiment using existing components is a pattern recognition problem an AI is really good at, something TU Wen and his team learned when asking AI to do it's thing. Click here to view the paper.