Quantum Interference

FROM THE UNIVERSITY OF VIENNA

As Richard Feynman famously put it, “the double slit experiment is absolutely impossible to explain in any classical way and has in it the heart of quantum mechanics. In reality, it contains the only mystery.”

Indeed, in this experiment, a quantum particle behaves as if it was at two distinct locations at the same time, and exhibits paradigmatic wave-like phenomena such as interference. However, it was later noted that multi-slit experiments show that the degree of delocalization of quantum particles has its limits, and that in a certain sense, quantum particles cannot be simultaneously delocalized at more than two locations.

This limitation has created a puzzle that to this day has not yet been completely resolved. Researchers at the University of Vienna and IQOQI-Vienna (Austrian Academy of Sciences) have made a significant step towards understanding this problem by reformulating interference experiments in terms of information-theoretic games. Their analysis, which has recently appeared in the journal Quantum, provides an intuitive way of thinking about interference phenomena and its limitations, thereby paving the way towards solving the aforementioned puzzle.


One of the most striking features of quantum mechanics is the superposition principle. This principle can be most easily illustrated via the double-slit experiment, which involves a particle that is sent through a plate pierced with two slits. According to our common everyday intuitions, one might expect the particle to always pass either through one slit, or through the other.

However, quantum mechanics implies that the particle can in a certain sense pass through both slits at the same time, that is, it can be in a superposition of two locations at the same time. This possibility underlies the phenomenon of quantum interference, i.e. the striking wave-like behavior exhibited by quantum particles. Now, is there a way to quantify the degree to which quantum particles can be de-localized? Does quantum theory allow particles to traverse more than two paths at the same time? In order to understand these questions, physicists have analyzed “multi-slit experiments” (Sorkin, Rafael D. “Quantum mechanics as quantum measure theory.”  READ MORE

Machine Learning

As reported by Leah Crane:

Machine learning, a process used to train artificial intelligences, can take an extremely long time – but a quantum trick could massively speed things up for tasks involving particles of light called photons.

In reinforcement learning, an algorithm runs through the same problem over and over again and is given a numerical reward only when it reaches the correct answer. That process teaches it to find the correct answer more quickly when pitted against similar problems later on.

Now Valeria Saggio at the University of Vienna in Austria and her colleagues have added a quantum twist to accelerate this process. They set up an experiment involving a photon moving through a wave guide and ending up in one of four possible states. They tasked an AI with making sure the photon ended up in one particular state, and rewarded it for doing so.

In the classical version of this experiment, without any added quantum effects, the AI would only be able to move the photon to one specific state at a time, being rewarded when it made a correct guess. However, in the quantum version of the experiment, the AI could put the photon in a superposition of more than one state. This allowed it to narrow down the correct answer before making a final, classical guess at the goal state.

“Imagine you have a robot that is standing at a crossroads, and the robot has two options – it can go left or it can go right,” says Saggio. “If the robot goes right, it does not receive a reward, but if it goes left it receives a reward. At the next round, the probability of it going left will increase.”

That’s the classical version of the experiment, but the quantum version would allow it to go left and right simultaneously at each guess, requiring far fewer guesses before it learns to always go left. This strategy sped up the learning time of the AI by 63 per cent, from 270 guesses to just 100.