Spatial light modulation is vital for precision control in optics applications such as quantum computing, quantum simulation, and quantum metrology, but at ultraviolet wavelengths it has been hampered by device degradation and limited switching speed. This work overcomes those bottlenecks by employing a reflection‑based piston-micromirror array to shape UV light fields, correct aberrations, and switch patterns in sync with laser pulses at 1 kHz, without adding detectable phase noise. The method yields e.g. optical tweezers with subpercent intensity variation, and exceptionally uniform flat‑top beams, opening up the UV spectral range for applications based on arbitrary light shaping.

Now open for business: Characterizing open quantum systems is essential for advancing quantum technologies, where noise from the environment continues to limit performance. Progress in addressing this important issue has been hindered by the lack of scalable techniques to learn both coherent and dissipative dynamics from data. This study extends the Quantum Model Learning Agent framework to open systems, using the Lindblad master equation. By combining Bayesian inference with a genetic algorithm, the method enables automated, hardware-informed identification of noise processes. The results enable more precise calibration and control in real-world hardware implementations.

High-quality, single-crystal van der Waals (vdW) materials provide a promising platform for constructing Josephson junctions, but systematic studies have been limited by fabrication and measurement challenges. Here researchers characterize 24 vertical vdW superconductor-semiconductor junctions, including microwave spectroscopy of an all-vdW transmon qubit. Transport measurements reveal a crossover from proximity- to tunneling-type behavior with increasing semiconductor thickness. The results demonstrate how band alignment and materials engineering can be used to tailor qubit properties, establishing vdW heterostructures as an emerging platform for next-generation superconducting qubits.

Altermagnets (collinear antiferromagnets that exhibit some of the properties of ferromagnets) hold promise for future technologies. Despite many theoretical predictions, however, confirming candidate materials remains challenging, and most experimental studies have been limited to thin films or surfaces. Here the authors use nanoscale transmission x-ray dichroic imaging to confirm the bulk altermagnetic nature of one of the most promising candidates, MnTe, in the absence of surface and strain effects. Nanoscale topological textures are observed to occur spontaneously in MnTe. This promising approach offers a route to explore other candidate altermagnets, going forward.

Inertial sensing underpins many navigation techniques, providing location information between position fixes. Atom interferometry has long been a candidate for superior inertial sensors, enabling improvements in short-term sensitivity and long-term stability, but realizing this potential requires understanding and mitigation of error sources, particularly in dynamic environments. This study demonstrates suppression of dynamic phase errors via rapid reversal of the direction of inertial sensitivity, at a rate much faster than the measurement bandwidth of the interferometer. The resulting noise resilience should prove useful in field applications of matter-wave interferometers.

Spin qubits in gate-defined semiconductor quantum dots are a versatile platform for quantum computation and simulation, owing to their flexible operation and compatibility with CMOS foundry processes. Unfortunately, capacitive crosstalk—particularly via the exchange interaction between adjacent spins—is an ongoing issue. The authors study a 2×4 array of hole-spin qubits in Ge and find an easily tracked constant-exchange signature, to precisely quantify and compensate the crosstalk. They also note patterns tied to device geometry and fabrication processes. Their findings provide a method to benchmark exchange crosstalk, and suggest best practices for designing future large-scale devices.

Light-matter interaction at the level of a single photon and atom is the core of quantum technologies for interfacing material qubits to “flying” qubits. Although loss remains a significant limitation in the optical domain, quantum squeezing may be used to enhance the light-matter interaction. This approach typically assumes a perfect bath with infinite bandwidth and no intrinsic cavity loss, which fails to capture realistic experimental conditions. In this work the authors develop a model that explains when squeezing may or may not assist in improving light-matter interaction, and they outline possible experimental platforms to attain squeezing-enhanced coupling.

Applying magnetic flux to manipulate qubits is an important method in superconducting quantum computing, but the state-of-the-art approach based on room-temperature electronics suffers from some unscalable limitations. This work provides an alternative approach in which an rf SQUID serves as an on-chip source of direct current, to provide qubits with in situ, low-noise magnetic flux. Several single-pulse inputs are enough to modulate the source and provide qubits with flux; this can benefit from time-division multiplexing to save on cables and sources. The technique could provide a scalable solution for applying magnetic flux in fault-tolerant quantum computing.

Open-loop quantum optimal control is a powerful technique to realize fast, high-fidelity quantum operations. Its successful implementation in real-world scenarios is limited, however, because it relies on a model of quantum dynamics that cannot attain the desired precision. This study uses physics-inspired machine learning to solve the problem, by inferring an accurate model of the dynamics from experimental data. The approach provides both a useful characterization of the system’s behavior and the optimal controls to realize arbitrary operations on it, and thus is a valuable tool for quantum information processing.

Phase-change materials such as VO${}_2$ offer exciting possibilities for thermal information processing and energy conversion, thanks to their tunable thermal properties that enable control of far-field radiative heat flow. This study presents a VO${}_2$-based radiative thermal transistor that yields 100× amplification of far-field heat current. Along the way, the authors uncover how the phase-transition behavior, shaped by VO${}_2$’s microstructure and intrinsic properties, directly influences radiative heat transfer. This work could enable the development of thermal logic circuits, smarter heat management, and innovative thermal devices with enhanced performance.

Phase-change materials such as VO${}_2$ offer exciting possibilities for thermal information processing and energy conversion, thanks to their tunable thermal properties that enable control of far-field radiative heat flow. This study presents a VO${}_2$-based radiative thermal transistor that yields 100× amplification of far-field heat current. Along the way, the authors uncover how the phase-transition behavior, shaped by VO${}_2$’s microstructure and intrinsic properties, directly influences radiative heat transfer. This work could enable the development of thermal logic circuits, smarter heat management, and innovative thermal devices with enhanced performance.

Can a small, versatile machine be trained on the time series of a few elements in a large, heterogeneous complex system, and then substitute any element while preserving its collective dynamics over a finite time horizon? Leveraging the technique of reservoir computing (RC) in machine learning, the authors demonstrate that such a machine can indeed be constructed, and performs effectively in general complex networks of heterogeneous dynamics and structure. The all-in-one, plug-and-play nature of versatile RC offers substantial convenience for maintaining the functionality of real-world complex systems that suffer sporadic or temporary component failures.

The authors show how specific geometric arrangement of finite-sized artificial square-ice structures directly governs their energy-relaxation pathways, and thus their probability of finding low-energy states. By systematically combining and modifying basic building blocks (4-loop and 4-vertex motifs), embedded and connected configurations can be designed to favor either monotonic (quasi-deterministic) or intermittent (probabilistic) relaxation. Dipolar energy calculations and magnetic force microscopy support the findings. This research provides a design principle for creating spin ices for future computing applications.

The superconducting diode effect, characterized by asymmetrical critical currents in opposite directions, is interesting in terms of both fundamental physics and potential for quantum electronics. This Letter offers a mechanism for the effect, based on a nonequilibrium theoretical framework that goes beyond conventional approaches. This approach allows the authors to incorporate the coupling between dissipative currents and time-reversal-symmetry-breaking order parameters, which can stabilize very strong nonreciprocity. While the mechanism is specifically illustrated for systems with two valleys, such as multilayer graphene, it remains more generally applicable.

While gallium oxide offers major potential for power electronics, its poor thermal conductivity makes heat dissipation challenging. This study presents a cooling strategy centered on the heterogeneous integration of gallium oxide devices with cubic boron arsenide ($c$-BAs). Using molecular dynamics simulations with machine-learned interatomic potentials, the authors find remarkable interfacial thermal conductance; this plus the ultrahigh thermal conductivity of $c$-BAs yields notable reduction in device temperature. This work highlights $c$-BAs as an outstanding material for cooling tomorrow’s chips, and may facilitate the engineering of interfacial thermal transport at the atomic scale.

Now open for business: Characterizing open quantum systems is essential for advancing quantum technologies, where noise from the environment continues to limit performance. Progress in addressing this important issue has been hindered by the lack of scalable techniques to learn both coherent and dissipative dynamics from data. This study extends the Quantum Model Learning Agent framework to open systems, using the Lindblad master equation. By combining Bayesian inference with a genetic algorithm, the method enables automated, hardware-informed identification of noise processes. The results enable more precise calibration and control in real-world hardware implementations.

High-quality, single-crystal van der Waals (vdW) materials provide a promising platform for constructing Josephson junctions, but systematic studies have been limited by fabrication and measurement challenges. Here researchers characterize 24 vertical vdW superconductor-semiconductor junctions, including microwave spectroscopy of an all-vdW transmon qubit. Transport measurements reveal a crossover from proximity- to tunneling-type behavior with increasing semiconductor thickness. The results demonstrate how band alignment and materials engineering can be used to tailor qubit properties, establishing vdW heterostructures as an emerging platform for next-generation superconducting qubits.

Domain-wall (DW) devices have garnered interest for diverse applications including memory, logic, and neuromorphic primitives; thus fast, accurate device models are imperative. Existing models of DW motion are suboptimal for large systems: They either devour computational resources, or oversimplify the physics. The authors propose a DW model inspired by the phenomenological similarities between the motion of a DW and that of a classical object subject to forces like friction or drag. This model predicts essentially the same DW motion as do micromagnetic simulations, but 4000× as fast. It is also faster than collective-coordinate models, and much more accurate than hyper-reduced models.

Spatial light modulation is vital for precision control in optics applications such as quantum computing, quantum simulation, and quantum metrology, but at ultraviolet wavelengths it has been hampered by device degradation and limited switching speed. This work overcomes those bottlenecks by employing a reflection‑based piston-micromirror array to shape UV light fields, correct aberrations, and switch patterns in sync with laser pulses at 1 kHz, without adding detectable phase noise. The method yields e.g. optical tweezers with subpercent intensity variation, and exceptionally uniform flat‑top beams, opening up the UV spectral range for applications based on arbitrary light shaping.

Altermagnets (collinear antiferromagnets that exhibit some of the properties of ferromagnets) hold promise for future technologies. Despite many theoretical predictions, however, confirming candidate materials remains challenging, and most experimental studies have been limited to thin films or surfaces. Here the authors use nanoscale transmission x-ray dichroic imaging to confirm the bulk altermagnetic nature of one of the most promising candidates, MnTe, in the absence of surface and strain effects. Nanoscale topological textures are observed to occur spontaneously in MnTe. This promising approach offers a route to explore other candidate altermagnets, going forward.

Inertial sensing underpins many navigation techniques, providing location information between position fixes. Atom interferometry has long been a candidate for superior inertial sensors, enabling improvements in short-term sensitivity and long-term stability, but realizing this potential requires understanding and mitigation of error sources, particularly in dynamic environments. This study demonstrates suppression of dynamic phase errors via rapid reversal of the direction of inertial sensitivity, at a rate much faster than the measurement bandwidth of the interferometer. The resulting noise resilience should prove useful in field applications of matter-wave interferometers.

Spin qubits in gate-defined semiconductor quantum dots are a versatile platform for quantum computation and simulation, owing to their flexible operation and compatibility with CMOS foundry processes. Unfortunately, capacitive crosstalk—particularly via the exchange interaction between adjacent spins—is an ongoing issue. The authors study a 2×4 array of hole-spin qubits in Ge and find an easily tracked constant-exchange signature, to precisely quantify and compensate the crosstalk. They also note patterns tied to device geometry and fabrication processes. Their findings provide a method to benchmark exchange crosstalk, and suggest best practices for designing future large-scale devices.

Light-matter interaction at the level of a single photon and atom is the core of quantum technologies for interfacing material qubits to “flying” qubits. Although loss remains a significant limitation in the optical domain, quantum squeezing may be used to enhance the light-matter interaction. This approach typically assumes a perfect bath with infinite bandwidth and no intrinsic cavity loss, which fails to capture realistic experimental conditions. In this work the authors develop a model that explains when squeezing may or may not assist in improving light-matter interaction, and they outline possible experimental platforms to attain squeezing-enhanced coupling.

Applying magnetic flux to manipulate qubits is an important method in superconducting quantum computing, but the state-of-the-art approach based on room-temperature electronics suffers from some unscalable limitations. This work provides an alternative approach in which an rf SQUID serves as an on-chip source of direct current, to provide qubits with in situ, low-noise magnetic flux. Several single-pulse inputs are enough to modulate the source and provide qubits with flux; this can benefit from time-division multiplexing to save on cables and sources. The technique could provide a scalable solution for applying magnetic flux in fault-tolerant quantum computing.

Open-loop quantum optimal control is a powerful technique to realize fast, high-fidelity quantum operations. Its successful implementation in real-world scenarios is limited, however, because it relies on a model of quantum dynamics that cannot attain the desired precision. This study uses physics-inspired machine learning to solve the problem, by inferring an accurate model of the dynamics from experimental data. The approach provides both a useful characterization of the system’s behavior and the optimal controls to realize arbitrary operations on it, and thus is a valuable tool for quantum information processing.

Wave dynamics offer intrinsic parallelism that remains largely untapped in current wave-based computing systems. This work proposes a framework for a dynamical wave-propagating network to tackle combinatorial optimization. By empowering both nodes and edges to actively process signals through frequency mixing and programmable time delays, this technique exploits parallelism across frequency, space, and time. The approach is validated on canonical benchmarks—including number partitioning, the 0/1 knapsack problem, and the traveling-salesman problem—while rigorously addressing practical constraints such as pseudopolynomial complexity and energy density.