A New Study Says Quantum Entanglement May Be Reversible
"Hearst Magazines and Yahoo may earn commission or revenue on some items through these links."
Time might kind of flow backward for quantum systems, due to present but lower “entropy.”
In a new paper, scientists model quantum “entropy” using a generous probabilistic model.
This means their holdings could be true for weaker, or less generous, models of these effects.
We all know “things fall apart” in the universe due to entropy, but quantum mechanics has turned a lot of what we’ve believed on its head in the last 100—and even 10—years. At the same time, it doesn’t make intuitive sense that a quantum entanglement could last forever, because nothing seems to in our universe. In new research, scientists suggest that quantum entanglement is at least somewhat reversible, by identifying and defining a version of entropy that applies to quantum entanglement. And they made it work using probabilities.
Bartosz Regula from the RIKEN Center for Quantum Computing and Ludovico Lami from the University of Amsterdam collaborated on new, peer-reviewed research published in the journal Nature Communications. In their paper, they summarize one of the thornier questions of quantum mechanics: Can everything in a quantum entanglement be meaningfully reversed, pointing “time’s arrow” in the opposite direction? This would mean a concept or system that had no entropy, or tendency towards disorder. Instead of a spilled glass of water or squeezed tube of toothpaste, a quantum system would be more like a neat seesaw you can move back to starting position.
If you have two classical (not quantum) systems with equal entropy, the scientists explain, then you can compare them directly with specific kinds of calculations. That’s because entropy itself is “a unique resource measure.”. Many people may think of entropy purely as a concept, but for physicists, it’s also a variable and a key descriptor that smooths the working equations describing our universe. In this case, “two comparable states of equal entropy can always be connected by a reversible adiabatic transformation,” they explain—an exchange of energy that can be reversed.
When physicists began to see signs of reversibility in quantum systems, they started to debate whether or not quantum entropy existed. A clean, calculable version of quantum entropy would become a key variable the same way it has for classical systems, giving scientists a toehold to compare different states and draw conclusions. Today, the growing field of quantum computing is hindered somewhat by how difficult it is to compare systems and make simple, generalized measurements for talking about those systems.
Regula and Lami have built a mathematical model that uses probabilities rather than pure mathematical certainties in the form of equations in order to probe these questions. And it’s not just a fuzzier version of an existing equation—rather, its a true alternative that goes for the same goals. Staking out some concept of entropy and a suggestion for how to measure it is a great start for others to recreate and further in their own research.
Probabilities are all around us, and in pop culture, they’re often flattened upward or downward to 100% or 0%. In reality, very few things are so stark. On TV shows, experts consider 10 possible sites for a crime, do some handwaving probabilities, then send all their resources to one place that turns out to be right. This can make it seem less impressive that real-life investigators can use careful observation and likelihoods to cut the number from 10 down to 6 or 7.
Indeed, that’s where the researchers conclude their paper. “This may not be enough to ensure the existence of a repeatable, reversible transformation cycle in practice. Nevertheless, we regard our results as evidence that reversibility could indeed be recovered also in the deterministic setting,” they explain. “ We hope that our results stimulate further research in this direction, leading to an eventual resolution of the open questions.”
In the long pursuit of scientific research goals, probabilities have a lot of value. Even papers describing a tried and failed new model can be really useful, or even essential to unlocking the next step to understanding. But probabilities in particular are thriving today, because computing power lets scientists describe and graph out more and more possibilities.
You Might Also Like
