Strain engineering in group-IV semiconductors, in particular germanium (Ge), offers a promising route for advancing next-generation optoelectronic device performance. High minority carrier lifetime serves as a direct indicator of superior material quality and device efficiency. Several factors govern the minority carrier lifetime in both strained $\mathrm{Ge}$ and strained $\mathrm{Ge}$$\mathrm{Sn}$, such as strain level, epilayer thickness, defects, surface roughness, and $\mathrm{Sn}$ alloying. In this study, the impact of biaxial strain on effective minority carrier lifetimes of tensile-strained $\mathrm{Ge}$ (0% $\epsilon$-$\mathrm{Ge}$/$\mathrm{Al}\mathrm{As}$, 0.78% $\epsilon$-$\mathrm{Ge}/{\mathrm{In}}_{0.115}{\mathrm{Ga}}_{0.885}\mathrm{As}$, and 0.93% $\epsilon$-$\mathrm{Ge}/{\mathrm{In}}_{0.16}{\mathrm{Ga}}_{0.84}\mathrm{As}$) and compressively strained $\mathrm{Ge}$$\mathrm{Sn}$ ($-0.53\mathrm{\%}c$-${\mathrm{Ge}}_{0.97}{\mathrm{Sn}}_{0.03}$/$\mathrm{Al}\mathrm{As}$) epilayers, grown by molecular beam epitaxy, was experimentally investigated via the microwave-reflection photoconductive decay ($\mu$-PCD) technique as a simultaneous function of thickness and strain. The structural and strain-relaxation properties were analyzed by high-resolution x-ray diffraction and Raman spectroscopy, while surface morphology analysis using atomic force microscopy provided insights into the surface roughness of strained $\mathrm{Ge}$-based epilayers. Defect characterization by cross-section transmission electron microscopy revealed superior crystalline quality of $\epsilon$-$\mathrm{Ge}$ in one epilayer, while twin boundary formation was observed in the other. We investigated the carrier recombination dynamics in epitaxially strained $\mathrm{Ge}$ by extracting the bulk recombination lifetime and surface recombination velocity using the thickness-dependent minority carrier lifetime. High effective minority carrier lifetimes of approximately $60–340\mathrm{ns}$ were achieved at high strain levels with minimal dependence on the thickness of the strained $\mathrm{Ge}$-based epilayers by means of the $\mu$-PCD technique. Minority carrier lifetimes in $\mathrm{Ge}$-based epilayers exhibit a quadratic dependence on the biaxial strain, ranging from $-0.88\mathrm{\%}$ (compressive) to $+1.6\mathrm{\%}$ (tensile). The analysis of the carrier recombination dynamics in strained $\mathrm{Ge}$-based heterostructures offers a pathway to designing group-IV based optoelectronic devices.

We investigate the collective dynamics of a three-level ensemble under the Dicke limit, revealing a unified connection between superradiant emission and electromagnetically induced transparency (EIT). Our results show that the transient superradiant burst exhibits the expected peak intensity scaling $I_{max}\sim N^2$, with a universal finite-size correction $|\xi (N)-2|\sim 1/\ln N$ that governs the apparent scaling exponent in realistic ensembles. In the stationary regime, collective broadening modifies the EIT response: although it typically enhances absorption, it counterintuitively increases the group velocity, leading to a relative scaling $v_g\propto N^2$, even while $v_g\ll c$. This effect suggests that cooperative interactions fundamentally limit the achievable slow-light delay in dense media. To achieve these results, we derive a representative-atom master equation that quantitatively reproduces both the superradiant and EIT regimes, in excellent agreement with the exact symmetric-subspace dynamics and correctly incorporating collective feedback and $N$-dependent broadening. This unified framework bridges transient superradiant emission and steady-state quantum interference, with direct implications for slow light, quantum memories, and precision metrology.

Dirac-like band crossings are paradigms in condensed-matter systems to emulate high-energy physics phenomena. They are associated with two aspects: gap and tilting. The ability to design a sign-changing gap gives rise to band topology, whereas the tilting of band crossings, which is a gateway for large gravitylike effects remains uncharted. In this work, we introduce an experimental platform to realize tilted Dirac-like microwave cone in large-scale superconducting circuit lattices. The direction and magnitude of the tilt can be controlled by engineering the axially preferred second-neighbor coupling. We demonstrate three lattices with 731-site $LC$ resonator featuring tilt values of up to 59% of relative difference in the opposite-direction group velocities. This is obtained by reconstructing the density of states (DOS) of measured microwave resonance frequencies. Harnessing the tilt of Dirac-like band crossings lays the foundation for weaving the fabric of an emergent solid-state spacetime.

As a branch of quantum machine learning, quantum reinforcement learning (QRL) aims to solve complex sequential decision-making problems more efficiently and effectively than its classical counterpart by exploiting quantum resources. However, in the noisy intermediate-scale quantum (NISQ) era, its realization is challenged by the ubiquitous noise-induced decoherence. Here, we propose a noise-resilient QRL scheme for a quantum eigensolver with a two-level system as an agent. By investigating the non-Markovian decoherence effect on the QRL for solving the eigenstates of the agent-environment interaction Hamiltonian, we find that the formation of a bound state in the energy spectrum of the total agent-noise system restores the QRL performance to that in the noiseless case. Providing a universal physical mechanism to suppress the decoherence effect on quantum machine learning, our result lays the foundation for designing NISQ algorithms and offers a guideline for their practical implementation.

Voltage-programmable transistor and diode operations are demonstrated within a single skyrmionic device structure via strain-mediated magnetoelectric coupling. On the basis of electromechanical and micromagnetic simulations, the spatial strain distributions are precisely tuned through gate voltages to achieve directional control and cutoff states of skyrmion motion. This mechanism allows both the operational modes and functional directionality to be programmed solely through electrical stimuli. The proposed approach eliminates Joule heating and achieves subfemtojoule energy consumption, effectively reducing device complexity by integrating multiple electronic functions into a single unit. These results highlight a promising route toward the development of ultralow-power, reconfigurable logic, and neuromorphic computing systems.

Many combinatorial optimization problems (COPs) are naturally expressed using variables that take on more than two discrete values. To solve such problems using Ising machines (IMs)—specialized analog or digital devices designed to solve COPs efficiently—these multivalued integers must be encoded using binary spin variables. A common approach is one-hot encoding, where each variable is represented by a group of spins constrained so that exactly one spin is in the “up” state. However, this encoding introduces energy barriers: changing an integer’s value requires flipping two spins and passing through an invalid intermediate state. This creates rugged energy landscapes that may hinder optimization. We propose a higher-order Ising formulation for max-3-cut, a canonical COP involving integers with three possible states that provides a minimal setting for assessing multivalued formulations on IMs. Our formulation preserves valid configurations under single-spin updates. The resulting energy landscapes are smoother, and we show that this remains true even when the binary variables are relaxed to continuous values, making it well suited for analog IMs as well. Benchmarking on such an IM, we find that the higher-order formulation leads to higher success rates and faster time-to-solution than when one-hot encoding is employed.

Two-dimensional semiconductors are promising channel materials for continuing transistor scaling and extending Moore’s law. However, as transistor density increases, the area available for contacts shrinks, making low-resistance contacts a critical challenge. Here, we theoretically investigate the performance and scalability of edge, top, and hybrid contacts to ${\mathrm{Mo}\mathrm{S}}_2$ monolayers. Using a quantum transport model that includes key mechanisms such as image-force barrier lowering as well as open vertical metal leads, we compare the contact resistance of these geometries while analyzing the impact of parameters such as doping concentration, surrounding dielectrics, metal work functions, van der Waals gap thickness, and contact length. We find that hybrid contacts generally yield the best performance, especially when scaled to contact lengths below 10 nm, achieving a contact resistance close to the quantum limit at doping concentrations above ${10}^{13}{\mathrm{cm}}^{-2}$. The edge contact exhibits the poorest performance under most circumstances, as it suffers from higher Schottky barriers and lacks mechanisms to reduce them. While the top contact performs well, the hybrid contact shows significantly lower resistances at extreme scaling because it is less sensitive to long transfer lengths.

Quantum machine learning (QML) has the potential to achieve quantum advantage for specific tasks by combining quantum computation with classical machine learning (ML). In classical ML, a significant challenge is membership-privacy leakage, whereby an attacker can infer from model outputs whether specific data were used in training. When specific data are required to be withdrawn, removing their influence from the trained model becomes necessary. Machine unlearning (MU) addresses this issue by enabling the model to forget the withdrawn data, thereby preventing membership-privacy leakage. However, this leakage remains underexplored in QML. This raises two research questions: do QML models leak membership privacy about their training data, and can MU methods efficiently mitigate such leakage in QML models? We investigate these questions using two quantum neural network (QNN) architectures, a basic QNN and a hybrid QNN, evaluated in noiseless simulations and cloud quantum device demonstrations. To answer the first question, we analyze how quantum constraints shape membership-privacy leakage in QML and then formalize a realistic gray-box threat model accordingly. Based on this, we design a membership inference attack (MIA) tailored to QNN outputs, and our results provide clear evidence of membership leakage in both QNNs. To answer the second question, we propose a quantum machine unlearning (QMU) framework, comprising three MU mechanisms. Evaluations on two QNN architectures show that QMU removes the influence of the withdrawn data while preserving accuracy for retained data. A comparative analysis further characterizes the three MU mechanisms with respect to data dependence, computational cost, and robustness. We further study how the shot count in quantum measurement affects both membership leakage and unlearning stability. Overall, this work provides a potential path toward privacy-preserving QML.

We propose a practical, physics-inspired benchmarking suite to challenge both quantum and classical computers by mapping real-time quantum dynamics to a common optimization format. Using a parallel-in-time encoding, we convert the real-time propagator of an $n$-qubit, possibly non-Hermitian, Hamiltonian into quadratic unconstrained binary optimization (QUBO) instances that are executable in a solver-agnostic manner on quantum annealers and classical optimizers alike. This enables direct, like-for-like performance comparisons across fundamentally different computational paradigms. To stress-test the framework, we consider eight representative dynamical models spanning single-qubit rotations, multiqubit entangling gates (Bell, Greenberger-Horne-Zeilinger, cluster), and $\mathcal{P}\mathcal{T}$-symmetric and other non-Hermitian generators, and evaluate success probability and time to solution as standard benchmarking metrics. Applying this method to two generations of D-Wave quantum annealers and to state-of-the-art classical solvers (simulated annealing and the graphics-processing unit–accelerated VeloxQ), we find that Advantage2 consistently outperforms its predecessor, while VeloxQ retains the shortest absolute run-times, reflecting the maturity of classical heuristics. We further extend the benchmarks to large-scale instances ($N\approx {10}^5$), establishing a demanding classical baseline for future hardware. Together, these results position the parallel-in-time QUBO framework as a versatile and physically motivated test bed for quantitatively tracking progress toward quantum-competitive simulation of dynamical systems.

The Gravity Recovery and Climate Experiment (GRACE) missions monitor variations in the Earth’s gravity field, primarily due to water transport, by measuring the change in the distance between two spacecraft. The next generation of GRACE-like missions is expected to rely on interspacecraft laser interferometry to recover these geodesy signals, necessitating the development of a technique to provide long-term laser frequency knowledge. A development electronics unit has been designed and manufactured to provide long-term absolute laser frequency knowledge for the GRACE-Continuity laser ranging system. This paper presents the first measurement of cavity ageing over 2.5 years using this technique and reports on the performance with sub-MHz errors in a GRACE-like optical cavity, sufficient for next-generation GRACE missions. Limitations caused by spurious etalons in the optical system are discussed.

Laser-sustained plasmas (LSPs), as broadband high-intensity light sources for wafer inspection, are currently limited by brightness instability from buoyancy-induced convective oscillations. In this work, we derive a vorticity-resolved scaling law for LSP oscillation frequencies $f_{\mathrm{os}}$, fully incorporating heat capacity $C_{\mathrm{p}}$, thermal conductivity $k_{\mathrm{T}}$, gravitational acceleration $g$, mass density $\rho$, and density ratio ${\sigma }_{\mathrm{den}}$, i.e., $f_{\mathrm{os}}=\mathcal{F}({\sigma }_{\mathrm{den}},g,C_{\mathrm{p}},k_{\mathrm{T}},\rho )$, providing a generalized scaling relation for LSP oscillations. The established hydro-thermal-laser-coupled simulations reveal that higher $(C_{\mathrm{p}},\rho )$ or lower $k_{\mathrm{T}}$ reduces vortex detachment height through thermal diffusivity modulation, while a larger $g$ theoretically amplifies the vortex transport, both increasing the LSP oscillation frequency. The generalized scaling law enables predictive control of LSP stability, enhancing the precision of high-resolution wafer optical inspection.

While geometric quantum gates are often theorized to possess intrinsic resilience to control errors by exploiting the global properties of evolution paths, this promise has not consistently translated into practical robustness. We present a streamlined framework for nonadiabatic geometric quantum gates (NGQGs) that incorporates additional auxiliary constraints to suppress dynamical contamination and achieve super-robust performance. Within this framework, we also design NGQGs using open paths, offering enhanced design flexibility. Implemented on superconducting transmon qubits, our scheme realizes high-fidelity single-qubit gates that are robust against Rabi amplitude error $ϵ$, with infidelity scaling as $\mathcal{O}({ϵ}^4)$, in contrast to the $\mathcal{O}({ϵ}^2)$ behavior of conventional dynamical gates. We further analyze two-qubit NGQGs under parametric driving. Our results identify subtle limitations that compromise performance in two-qubit scenarios, underscoring the significance of phase compensation and waveform calibration. The demonstrated simplicity and generality of our super-robust NGQG scheme make it applicable across diverse quantum platforms.

Ventilation metamaterials and metasurfaces have gained significant interest in recent years due to their excellent properties of passive sound insulation, while also allowing air to flow freely. However, previous designs based on Helmholtz or Fano-like resonances are limited by bandwidth, and empirical design approaches leave considerable room for optimization. The metasurface composed of square units, obtained through topology optimization based on a genetic algorithm, exhibits sound insulation properties. The agreement between experimental and numerical results demonstrates that the metasurface blocks over 90% of incident energy in the range of 447–2084 Hz, achieving a relative bandwidth of 1.6 at the subwavelength scale while maintaining a 20% ventilation rate. Such remarkable performance is attributed to the synergy of resonance and destructive interference. In addition, the Pareto front is calculated to identify optimal tradeoffs between ventilation efficiency and sound insulation bandwidth. Our design not only suppresses extreme transmission originating from the metasurface composed of square units for sound insulation but also provides optimized structures for different ventilation rates, paving a new path for effective noise reduction.

Decoherence in superconducting qubits is dominated by defects that reside at amorphous interfaces. Interaction with discrete defects results in dropouts that complicate qubit operation and lead to non-Gaussian tails in the distribution of qubit energy relaxation time $T_1$ that degrade system performance. Spectral diffusion of defects over time leads to fluctuations in $T_1$, posing a challenge for calibration. In this work, we measure the energy relaxation of flux-tunable transmons over a range of operating frequencies. We vary qubit geometry to change the interface participation ratio by more than an order of magnitude. Our results are consistent with loss dominated by discrete interfacial defects. Moreover, we are able to localize the dominant defects to within 500 nm of the qubit junctions, where residues from liftoff are present. These results motivate alternative approaches to qubit junction fabrication that avoid the residues intrinsic to the liftoff process.

Potential and mass barriers in graphene introduce electron scattering, modulating transmission probabilities. Complex multibarrier setups allow electron transmission to be controlled with high precision, but they have a huge design space of possible barrier geometries. This work presents a framework to optimize electronic transport in such systems using differential-evolution algorithms. First, transfer-matrix methods are employed to efficiently compute quantum transport through multibarrier structures, and optimization is then applied to find barrier configurations whose transmission profiles closely match a predefined target profile. To address the trade-off between the accuracy and complexity of the resulting barrier configurations, regularization techniques are incorporated into the optimization process. Our approach demonstrates the potential for highly tunable electronic transport in graphene-based systems by exploiting evolution-inspired optimization techniques.

Quantum computers require high-fidelity measurement of many qubits to achieve a quantum advantage. Traditional approaches suffer from readout crosstalk for a neutral-atom quantum processor with a tightly spaced array. Although classical machine learning algorithms based on convolutional neural networks can increase fidelity, they are computationally expensive, making it difficult to scale them to large qubit counts. We present two simpler and scalable machine learning algorithms that realize matched filters for the readout problem. One is a local model that focuses on a single qubit, and the other uses information from neighboring qubits in the array to prevent crosstalk among the qubits. We demonstrate error reductions of up to 32% and 43% for the site and array models, respectively, compared with a conventional Gaussian threshold approach. Additionally, our array model uses 2 orders of magnitude fewer trainable parameters and 4 orders of magnitude fewer multiplications and nonlinear function evaluations than a recent convolutional neural network approach, with only a minor (3.5%) increase in error across different readout times. Inference is more than 350 times faster for our local model and more than 540 times faster for our array model compared with the respective convolutional neural networks. Another strength of our approach is its physical interpretability: the learned filter can be visualized to provide insights into experimental imperfections. We also show that two existing convolutional neural network models can be pruned to have 70 times and 4000 times fewer parameters, respectively, while maintaining similar errors. Our work shows that simple machine learning approaches can achieve high-fidelity qubit measurements while remaining scalable to systems with larger qubit counts.