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Metamaterial chains learn new shapes by sharing data hinge to hinge

In a new Nature Physics publication, University of Amsterdam researchers introduce human-made materials that spring to life. These ‘metamaterials’ don’t just learn to change shape, but can autonomously adapt their shape-changing strategy, perform reflex actions and move around like living systems do.

Normal materials have fixed, predetermined responses when a force is applied to them, whereas robots have pre-programmed behaviors. In stark contrast, living materials such as cells and brainless organisms can adapt extremely well to changing conditions. Inspired by nature, the research team created synthetic materials—metamaterials—that learn and adapt without a central “brain.”

The worm-like metamaterials progressively learn how to change shape by being trained on examples. They can forget old shapes and learn new ones, or learn and remember multiple shapes at once and toggle between these shapes. This allows them to perform advanced tasks such as grabbing an object or moving around (locomotion).

A layered approach sharpens brain signals in optical imaging

Near-infrared spectroscopy, or fNIRS, offers a way to monitor brain activity without surgery or radiation by tracking changes in blood flow and oxygenation. Light sources placed on the scalp send near-infrared light into the head, and detectors measure the light that scatters back. Because this light must pass through the scalp and skull before reaching the brain, the measured signal always includes a mix of superficial and cerebral contributions. Separating those signals has long been a central challenge for fNIRS researchers.

In a study published in Biophotonics Discovery, researchers from the Tufts University Diffuse Optical Imaging of Tissue Laboratory show that combining a specific source–detector geometry with a simple, anatomically informed tissue model can substantially improve how fNIRS data are interpreted.

By accounting for how light travels through layered head structures, the approach makes it possible to better isolate brain-specific signals without relying on complex imaging systems or subject-specific MRI scans.

Quantum computing without interruptions

Mid-circuit measurements are one of the biggest practical hurdles in quantum error correction on encoded qubits. Researchers in Innsbruck and Aachen have now proposed and experimentally demonstrated that a universal fault-tolerant quantum algorithm can be executed without such measurements. Using a trapped-ion quantum processor, the team successfully ran Grover’s quantum search algorithm on three logical qubits.

A key bottleneck in today’s leading approaches to quantum error correction is the need to repeatedly pause and measure the quantum processor mid-computation, a process that is slow, technically demanding, and itself a significant source of errors.

Now, a joint team from the University of Innsbruck, RWTH Aachen University, Forschungszentrum Jülich and spin-off Alpine Quantum Technologies (AQT) has demonstrated fault-tolerant quantum computation without any such interruptions.

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