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Intermolecular interactions are the forces that pertain between molecules. In general, these interactions scarcely extend beyond the boundaries of molecules. For the most part, they are effective over distances of less than 1 nanometer (10-9 m).

The largest distances discovered to date were in energy transmissions, where almost 10 nanometers were reached. A team led by LMU chemist Heinz Langhals has now found which, to the astonishment of the scientists, extend beyond 100 .

The researchers were able to demonstrate this using the concentration-dependent fluorescence decay time of dyes. “In this way, molecules can not only interact with their neighbors, but do so up to almost macroscopic dimensions,” says Langhals.

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Aug. 24, 2022 — Training a quantum neural network requires only a small amount of data, according to a new proof that upends previous assumptions stemming from classical computing’s huge appetite for data in machine learning, or artificial intelligence. The theorem has several direct applications, including more efficient compiling for quantum computers and distinguishing phases of matter for materials discovery.

“Many people believe that quantum machine learning will require a lot of data. We have rigorously shown that for many relevant problems, this is not the case,” said Lukasz Cincio, a quantum theorist at Los Alamos National Laboratory and co-author of the paper containing the proof published in the journal Nature Communications. “This provides new hope for quantum machine learning. We’re closing the gap between what we have today and what’s needed for quantum advantage, when quantum computers outperform classical computers.”

“The need for large data sets could have been a roadblock to quantum AI, but our work removes this roadblock. While other issues for quantum AI could still exist, at least now we know that the size of the data set is not an issue,” said Patrick Coles, a quantum theorist at the Laboratory and co-author of the paper.

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Researchers have demonstrated a way of measuring the electronic states of a material’s surface while avoiding signal contaminations from deeper layers.

The electronic states of a material’s surface might only be 2D, but they offer a depth of interesting physics. Such states, which are distinct from those of the material’s bulk, dominate many phenomena, such as electrical conduction, magnetism, and catalysis, and they are responsible for nontrivial surface effects found in topological materials and systems with strong spin-orbit interaction. Surface electronic states also control the properties of so-called 2D materials, such as graphene. To understand surface phenomena and harness them in practical devices, researchers chiefly rely on photoemission spectroscopy, which measures the energy and momentum of electrons emitted when photons hit the material. The high resolution with which electron energy and momentum can be characterized allows physicists to measure both the band structure and the density of states (DOS) in the few surface layers where escaping photoelectrons originate.

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