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Engineering a driven-dissipative bath of altermagnetic quantum magnons for controlling classical dynamics of spins hosting spin waves, domain walls, or skyrmions

Using quantum magnets to remotely control classical magnetic waves and patterns

Physicists have designed a way to control magnetic behavior in one material by attaching a quantum magnetic layer next to it. The quantum layer acts like a bath that damps and drives the classical magnetic material, creating new ways to tune how magnetic waves, domain walls, and skyrmions (tiny magnetic vortices) move and disappear. This could let engineers manipulate magnetic dynamics without direct electrical or magnetic contact.

Magnetic devices are central to data storage and computing, and most current approaches rely on direct control of the magnet itself. This technique offers a new handle for tuning magnetic behavior through an adjacent layer, potentially enabling more efficient or flexible designs for spintronic devices and magnonic circuits. It demonstrates a path to remotely shape how magnetic patterns propagate and annihilate, which matters for encoding and erasing information in next-generation magnetic memory.

Release-free electro-optomechanical crystal modulator

A better bridge between quantum computers and fiber optic networks

Researchers built a device that converts signals between microwave circuits in quantum computers and optical fibers with less thermal noise than previous designs. By combining two materials—silicon and lithium niobate—using a precise printing technique, they achieved the strong signal conversion needed for practical quantum-to-optical communication.

Quantum computers currently sit isolated on lab benches because they can't efficiently send information over long distances. This device could become the missing link that lets distant quantum computers talk to each other and to optical networks, making large-scale quantum computing infrastructure actually possible.

Note on Strong Quantum Markov Properties

When quantum systems reveal their secrets through local measurements

A quantum state satisfies a "strong Markov property" if you can recover lost information about it by measuring just one copy and applying a local fix — and this works the same way regardless of what you actually measure. The researchers show this property is equivalent to a simpler mathematical condition: correlations must decay in a particular way, and they prove three surprising consequences, including that you can estimate multiple properties of a quantum state from a single measurement.

Quantum systems are notoriously fragile and hard to measure. This result shows that under certain conditions — when a quantum state has the strong Markov property — you don't need many copies or elaborate measurement schemes to extract useful information. This could simplify how we extract information from quantum devices and systems in the lab, and it deepens our understanding of which quantum states are easier to work with in practice.

Quantum Lattice Boltzmann Solutions for Transport under 3D Spatially Varying Advection on Trapped Ion Hardware

Running fluid flow simulations on quantum computers with realistic conditions

Researchers demonstrated that quantum computers can simulate how fluids move and mix under varying flow patterns — a step toward realistic fluid dynamics calculations on quantum hardware. Using IonQ's trapped-ion systems, they solved the advection-diffusion equation in three dimensions and identified a major bottleneck: repeatedly reading out and reloading fluid density data. They propose using a technique called MPS shadow tomography to make this process faster at scale.

Quantum computers could eventually simulate complex fluid dynamics far faster than classical computers, with applications in aircraft design, weather prediction, and chemical engineering. This work moves beyond toy problems to conditions closer to what engineers actually need to model. However, the current readout bottleneck would need to be solved before quantum computers could outperform conventional supercomputers for these problems.