Quantum Entanglement Wins 2022’s Nobel Prize

Calendar October 6, 2022 | Posted by admin

For generations, scientists argued over whether there was truly an objective, predictable reality for even quantum particles, or whether quantum “weirdness” was inherent to physical systems. In the 1960s, John Stewart Bell developed an inequality describing the maximum possible statistical correlation between two entangled particles: Bell’s inequality. But certain experiments could violate Bell’s inequality, and these three pioneers —  John Clauser, Alain Aspect, and Anton Zeilinger — helped make quantum information systems a bona fide science.

There’s a simple but profound question that physicists, despite all we’ve learned about the Universe, cannot fundamentally answer: “What is real?” We know that particles exist, and we know that particles have certain properties when you measure them. But we also know that the very act of measuring a quantum state — or even allowing two quanta to interact with one another — can fundamentally alter or determine what you measure. An objective reality, devoid of the actions of an observer, does not appear to exist in any sort of fundamental way.

But that doesn’t mean there aren’t rules that nature must obey. Those rules exist, even if they’re difficult and counterintuitive to understand. Instead of arguing over one philosophical approach versus another to uncover the true quantum nature of reality, we can turn to properly-designed experiments. Even two entangled quantum states must obey certain rules, and that’s leading to the development of quantum information sciences: an emerging field with potentially revolutionary applications. 2022’s Nobel Prize in Physics was just announced, and it’s awarded to John Clauser, Alain Aspect, and Anton Zeilinger for the pioneering development of quantum information systems, entangled photons, and the violation of Bell’s inequalities. It’s a Nobel Prize that’s long overdue, and the science behind it is particularly mind-bending.

There are all sorts of experiments we can perform that illustrate the indeterminate nature of our quantum reality.

Place a number of radioactive atoms in a container and wait a specific amount of time. You can predict, on average, how many atoms will remain versus how many will have decayed, but you have no way of predicting which atoms will and won’t survive. We can only derive statistical probabilities.
Fire a series of particles through a narrowly spaced double slit and you’ll be able to predict what sort of interference pattern will arise on the screen behind it. However, for each individual particle, even when sent through the slits one at a time, you cannot predict where it will land.
Pass a series of particles (that possess quantum spin) through a magnetic field and half will deflect “up” while half deflect “down” along the direction of the field. If you don’t pass them through another, perpendicular magnet, they’ll maintain their spin orientation in that direction; if you do, however, their spin orientation will once again become randomized.
Certain aspects of quantum physics appear to be totally random. But are they really random, or do they only appear random because our information about these systems are limited, insufficient to reveal an underlying, deterministic reality? Ever since the dawn of quantum mechanics, physicists have argued over this, from Einstein to Bohr and beyond.

Source: https://bigthink.com/

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AI Designs Quantum Physics Beyond What Any Human Has Conceived

Calendar July 5, 2021 | Posted by admin

Quantum physicist Mario Krenn remembers sitting in a café in Vienna in early 2016, poring over computer printouts, trying to make sense of what MELVIN had found. MELVIN was a machine-learning algorithm Krenn had built, a kind of artificial intelligence. Its job was to mix and match the building blocks of standard quantum experiments and find solutions to new problems. And it did find many interesting ones. But there was one that made no sense.

“The first thing I thought was, ‘My program has a bug, because the solution cannot exist,’” Krenn says. MELVIN had seemingly solved the problem of creating highly complex entangled states involving multiple photons (entangled states being those that once made Albert Einstein invoke the specter of “spooky action at a distance”). Krenn, Anton Zeilinger of the University of Vienna and their colleagues had not explicitly provided MELVIN the rules needed to generate such complex states, yet it had found a way. Eventually, he realized that the algorithm had rediscovered a type of experimental arrangement that had been devised in the early 1990s. But those experiments had been much simpler. MELVIN had cracked a far more complex puzzle.

“When we understood what was going on, we were immediately able to generalize [the solution],” says Krenn, who is now at the University of Toronto. Since then, other teams have started performing the experiments identified by MELVIN, allowing them to test the conceptual underpinnings of quantum mechanics in new ways. Meanwhile Krenn, working with colleagues in Toronto, has refined their machine-learning algorithms. Their latest effort, an AI called THESEUS, has upped the ante: it is orders of magnitude faster than MELVIN, and humans can readily parse its output. While it would take Krenn and his colleagues days or even weeks to understand MELVIN’s meanderings, they can almost immediately figure out what THESEUS is saying.
“It is amazing work,” says theoretical quantum physicist Renato Renner of the Institute for Theoretical Physics at the Swiss Federal Institute of Technology Zurich, who reviewed a 2020 study about THESEUS but was not directly involved in these efforts.

Source: https://www.scientificamerican.com/

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