In the 1920s physicists laid the groundwork for quantum mechanics, which has since become a fundamental theory in physics. Quantum theory aims to explain the behavior of matter and light at the smallest scale, such as that of atoms and photons. Unlike in classical physics, these particles can exist simultaneously in different states and can be “entangled” with one another over vast distances. Entanglement theory is a central aspect of quantum mechanics and has paved the way for many modern technological advances, like computer chips, lasers, and quantum computers. It has also been instrumental in the development of secure communication and the understanding of quantum materials.

Until recently, it was a common assumption among scientists that entangled quantum states could be preserved and manipulated effectively in laboratory settings while performing completely general quantum operations with no other constraints than locality. Recent research suggests, however, that the practical applications of quantum entanglement, particularly in quantum computing, are more limited than previously believed. This can be attributed to computational constraints, even when conditions are ideal. “For a long time our understanding of entanglement was built upon idealized assumptions,” says Eisert. “Our work now shows that fundamental discrepancies arise in key quantum computing measurements when they are performed under realistic, which is to say efficient, conditions.”

This realization has led the team of researchers to introduce two key figures of merit in quantum physics: the computational distillable entanglement and the computational entanglement cost. These two figures quantify rates of entanglement in quantum states, in the form of entangled bits (ebits), within certain restricted conditions.

The study could fundamentally change scientists’ understanding of how quantum information is processed and emphasizes the importance of considering computational resources in performing these tasks. In addition, it presents a potential new solution for current problems surrounding efficiency in quantum computing – beyond just mere hardware issues. This approach forges a new link between theoretical computer science, which deals with the complexity and difficulty of computational operations, and physics, which attempts to understand nature.

Eisert also notes that their results point to a certain contradiction in quantum theory that has hitherto gone unnoticed. “It seems that two important research fields did not communicate enough with one another, namely, entanglement theory experts and quantum algorithm experts,” he says. Precisely this kind of exchange is at the heart of their current work, which highlights that exciting research questions often emerge at these points of contact between fields, or as Eisert puts it, “In the ‘cultural gaps’ or along the tectonic fault lines of established knowledge. Quantum theory is no exception here.”