The maximum-independent-set (MIS) problem is a fundamental combinatorial optimization task that can be naturally mapped onto the Ising Hamiltonian of neutral-atom quantum processors. Given its connection to NP-hard problems and real-world applications, there has been significant interest in exploring quantum advantage for the MIS problem. Pioneering experiments on King’s lattice graphs have suggested a quadratic speedup over simulated annealing; however, recent benchmarks using state-of-the-art methods found no clear advantage, likely due to the structured nature of the tested instances. In this work, we generate hard instances of unit-disk graphs by leveraging complexity-theory results and varying key hardness parameters such as density and treewidth. For a fixed graph size, we show that increasing these parameters reveals a large number of hard instances that can lead to prohibitive classical run-time increases of several orders of magnitude. We then compare classical and quantum approaches on small instances and find that, at this scale, quantum solutions are slower than classical ones for finding exact solutions. Based on extended classical benchmarks at larger problem sizes, we estimate that scaling up to 1000 atoms with a 1-kHz repetition rate is a necessary step toward demonstrating a computational advantage with quantum methods.

Kernel methods serve as powerful tools to capture nonlinear patterns behind data in machine learning. Quantum kernels, by mapping data into exponentially large Hilbert spaces, offer the potential to solve problems intractable for classical models. However, existing studies encounter performance bottlenecks when processing complex data with localized structural patterns, stemming from the limitation in single-view feature representation and the exclusive reliance on global data structure. In this paper, we propose quantum multiview kernel learning with local information, called L-QMVKL. Specifically, based on the multiple kernel learning, a representative method for multiview data processing, we construct the quantum multiple kernel that combines view-specific quantum kernels to effectively fuse cross-view information. Further leveraging local information to capture intrinsic structural information, we design a sequential training strategy for the quantum circuit parameters and combination weights with the use of the hybrid global-local kernel alignment. We evaluate the effectiveness of L-QMVKL through comprehensive numerical simulations on the Mfeat dataset, demonstrating significant accuracy improvements achieved through leveraging multiview methodology and local information. Meanwhile, the results show that L-QMVKL exhibits competitive accuracy compared to its classical counterparts. Our work holds promise for advancing the theoretical and practical understanding of quantum kernel methods.

Detecting the magnetic states of synthetic antiferromagnets (SAFs) at the nanoscale is challenging due to their low net magnetization. We demonstrate an electrical method based on tunneling magnetoresistance (TMR) in nanoscale magnetic tunnel junctions to selectively detect the magnetization of a single SAF sublayer. TMR hysteresis loops reveal distinct switching behaviors governed by the competition between magnetic anisotropy and interlayer exchange coupling. This sensitivity enables direct quantification of exchange coupling under both field- and spin-transfer torque-driven reversals, while accessing the overall SAF magnetic state independent of its net magnetization.

Acoustic holography has become a research hot spot in the field of acoustics due to its exceptional sound field manipulation capability, showing a great potential in biomedical engineering, acoustic communication, and additive manufacturing. Here we realize three-dimensional (3D) underwater holography by using a genetic algorithm–optimized binary metasurface. Our approach encompasses spatial discretization of a 3D target, global optimization by aid of the genetic algorithm equipped with an angular spectrum–based forward model, and reconstruction of the 3D target by sound intensity distribution. The effectiveness of our proposed method is verified through experimental demonstration. The achievement of a high-quality 3D acoustic hologram underwater has significant applications in 3D acoustic tweezing, high-capacity communication, and sound-empowered 3D bioprinting.

A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different results. As a result, their performance is evaluated over several repeats, or runs, on the problem. However, the accuracy of the estimated performance metrics depends on the number of runs and should be studied using statistical tools. We present a statistical analysis of the common metrics, and develop guidelines for experiment design to measure the optimizer’s performance using these metrics to a high level of confidence and accuracy. To this end, we first discuss the confidence interval of the metrics and how they are related to the number of runs of an experiment. We then derive a lower bound on the number of repeats in order to guarantee achieving a given accuracy in the metrics. Using this bound, we propose an algorithm to adaptively adjust the number of repeats needed to ensure the accuracy of the evaluated metric. Our simulation results demonstrate the utility of our analysis and how it allows us to conduct reliable benchmarking as well as hyperparameter tuning and prevents us from drawing premature conclusions regarding the performance of stochastic optimizers.

We report the experimental realization of a quantum silicon carbide microscope and demonstrate its functionality by imaging magnetic fields generated by electrical currents. We employ a dual-frequency sensing protocol to enhance the readout contrast and suppress noise arising from strain and temperature fluctuations. This approach enables spatial imaging of current-induced magnetic fields with a field of view of $50\times 50$ virtual pixels, temporal resolution of $50\mathrm{ms}$, spatial resolution of $30\text{μ}\mathrm{m}$, and sensitivity of about $2\text{μ}\mathrm{T}{\mathrm{Hz}}^{-1/2}$ per pixel. Further sensitivity enhancement is anticipated through the use of isotopically purified $\mathrm{Si}\mathrm{C}$ and improved light collection in crystallographically optimized wafer orientations. In addition, we implement a microwave-free imaging protocol based on a spin level anticrossing in $\mathrm{Si}\mathrm{C}$, offering simplified operation with enhanced sensitivity. The demonstrated platform is compatible with commercial wafer-scale fabrication and holds strong potential for applications in biomedical imaging and diagnostics, as well as noninvasive current and temperature mapping in high-power electronic devices.

Non-Gaussian low-frequency noise, such as random telegraph noise (RTN), is widely present in quantum sensing systems and is usually regarded as the main perturbation limiting sensitivity and stability. However, whether it can be transformed into a usable spectroscopic resource still lacks systematic experimental verification. We demonstrate $\mathrm{\Xi }$- and $\mathrm{\Lambda }$-type noise-induced transparency (NIT) effects in an optically pumped atomic magnetometer coupled to non-Gaussian RTN reservoirs, showing that NIT arises from interference between stochastic transition channels in spins, both theoretically and experimentally. In the $\mathrm{\Xi }$-type configuration, the spectral splitting driven by RTN exhibits a significant change in the transparency window, and the relationship between dynamic criticality and observable spectral structure is elucidated by the correlation between the Liouvillian exceptional point and the resolvable spectral-splitting threshold. In the $\mathrm{\Lambda }$-type configuration, the interference effect of amplitude-dependent noise enables the transparency effect to be stably manifested under weak noise, providing a calibrable noise-amplitude readout mechanism. These results extend magnetometer spectra from noise suppression to probing noise with noise, providing a new approach for non-Gaussian noise measurement and adaptive background-noise sensing.

The highly lyophobic perfluoropolymer Cytop is widely recognized as an excellent interfacial layer material for organic thin-film transistors (OTFTs), owing to its ability to eliminate interfacial traps and to enable steep subthreshold swing (SS), high carrier mobility, and stable operation. However, its strong lyophobicity poses a major challenge for subsequent solution-based processing, particularly in achieving high-resolution electrode patterning. In this study, we demonstrate that an extended meniscus technique enables direct photolithographic processing of photoresists on pristine Cytop. Uniform photoresist layers were deposited by blade coating under substrate heating, allowing fabrication of short-channel electrodes with lengths below 10 µm. Bottom-gate, bottom-contact OTFTs incorporating single-crystal organic semiconductor (OSC) thin films of phenyl/alkyl-substituted benzothieno[3,2-b]naphtho[2,3-b]thiophene exhibited steep SS values as low as 70–85 mV ${\mathrm{dec}}^{-1}$ and field-effect mobilities exceeding 1 ${\mathrm{cm}}^2$ ${\mathrm{V}}^{-1}$ ${\mathrm{s}}^{-1}$. The contact resistance, estimated from the channel-length dependence of the transfer characteristics, was approximately 1.5 kΩ cm. The gate-voltage dependence indicates that carrier accumulation at the OSC/electrode/dielectric ternary interface contributes to reducing contact resistance. This study represents a significant step toward realizing short-channel OTFTs with high mobility, low operating voltage, and stable performance.

Surface properties are critical in catalytic reactions, with increasing evidence that structural and mechanical factors, alongside electronic properties, play significant roles. Despite progress in catalyst design based on adsorption energy, these approaches often fail in practical applications because adsorption energy alone does not fully capture catalytic efficiency. The reaction barrier, a key factor in both thermodynamic and kinetic aspects, has often been overlooked. In this study, we introduce surface stress work $W_{\mathrm{SS}}$ as a quantitative descriptor for mechanical effects in surface reactions, which simultaneously influences both adsorption energy and reaction barriers. Using first-principles calculations on representative transition metals, we show that $W_{\mathrm{SS}}$ directly affects hydrogen adsorption energy and reaction barriers. Furthermore, we demonstrate that $W_{\mathrm{SS}}$ can be modulated via methods such as alloying, optimizing both adsorption energies and reaction barriers. $\mathrm{Pd}$-$\mathrm{Ni}$ alloys show the most significant improvements. These findings establish a quantitative concept for surface stress work in catalyst design, which applies not only to hydrogen-related reactions but also to oxygen-involving surface processes.

Van der Waals (vdW) heterostructures assembled from two-dimensional (2D) materials offer a highly versatile platform for developing multifunctional devices. Here we systematically investigate the electronic structure and photocatalytic properties of the $\beta$-$\mathrm{As}\mathrm{P}$/${\mathrm{Mo}\mathrm{Si}}_2{\mathrm{N}}_4$ vdW heterostructure via first-principles calculations. Our results indicate that the heterostructure remains stable at room temperature and possesses an indirect band gap of 1.91 eV. It exhibits a type-II band alignment, along with enhanced electron mobility and optical absorption capacity compared with its constituent monolayers. Notably, compressive strain exceeding 1% induces an indirect-to-direct band gap transition, attributed to the distinct strain responses of the band edges in the individual layers. Furthermore, the band alignment can be reversibly tuned between type I and type II through the application of external strain or an electric field. A particularly promising configuration arises under a tensile strain of 0.43% or an electric field of $-0.25$ V/Å, which results in equal band offsets ($\mathrm{\Delta }{E}_c=\mathrm{\Delta }{E}_v$). Our findings suggest great potential for low-power electronic device applications of 2D materials.

Photovoltaic materials facilitate the conversion of sunlight into electricity by harnessing the interaction between light and matter, offering an eco-friendly and cost-efficient energy solution. Combining data-driven approaches with static and time-dependent density-functional theories and nonadiabatic molecular dynamics simulations, we predict 14 high-performance photoabsorber materials from a family of known quaternary semiconductors. Among these, we investigate four compounds—$\mathrm{Sr}\mathrm{Cu}\mathrm{Gd}\mathrm{Se}$${}_3$, $\mathrm{Sr}\mathrm{Cu}\mathrm{Dy}\mathrm{Te}$${}_3$, $\mathrm{Ba}\mathrm{Cu}\mathrm{La}\mathrm{Se}$${}_3$, and $\mathrm{Ba}\mathrm{Cu}\mathrm{La}\mathrm{Te}$${}_3$—in greater detail. Their crystal structures reveal that $\mathrm{Cu}$ and $\mathrm{Gd}$/$\mathrm{La}$/$\mathrm{Dy}$ possess tetrahedral and octahedral coordination environments, respectively. Such bonding features suggest that these materials would possess optical activity, defect tolerance, and stability complementary to the known high-performance photoabsorber materials with either tetrahedral (e.g., $\mathrm{Si}$, $\mathrm{Ga}\mathrm{As}$, $\mathrm{Cd}\mathrm{Te}$) or octahedral (e.g., methylammonium lead iodide) coordination chemistries. Hybrid density-functional theory calculations including spin-orbit coupling reveal that $\mathrm{Sr}\mathrm{Cu}\mathrm{Gd}\mathrm{Se}$${}_3$, $\mathrm{Sr}\mathrm{Cu}\mathrm{Dy}\mathrm{Te}$${}_3$, $\mathrm{Ba}\mathrm{Cu}\mathrm{La}\mathrm{Se}$${}_3$, and $\mathrm{Ba}\mathrm{Cu}\mathrm{La}\mathrm{Te}$${}_3$ possess direct band gaps of 1.65, 1.01, 1.79, and 1.05 eV, respectively. These band-gap values lie close to an optimal range ideal for visible-light absorption. Consequently, the calculated optical absorption coefficient and spectroscopic limited maximum efficiency for these compounds become comparable to, or larger than, those of crystalline silicon, $\mathrm{Ga}\mathrm{As}$, and methylammonium lead iodide. The calculated exciton binding energies for these compounds are relatively small (9–80 meV), signifying easy separation of the electron-hole pairs and hence enhanced power-conversion efficiencies. Investigations of photoexcited carrier dynamics reveal a relatively long carrier lifetime (approximately 30–40 ns), suggesting suppressed nonradiative recombination and enhanced photoconversion efficiencies. Temperature-dependent structural dynamics and electronic band energies reveal relatively low local structural fluctuation, signifying their high stability and small electronic-ionic sublattice coupling, leading to a weaker nonradiative recombination process for photogenerated charge carriers, which is beneficial for high-performance photovoltaics. We have further determined the defect-formation energies in these compounds, which show that despite the likely formation of cation vacancy and antisite defects, midgap states remain absent, making these defects nondetrimental to carrier recombination. Our theoretical predictions invite experimental verification and encourage further investigations of these and similar compounds in this quaternary-semiconductor family.

Reconfigurable photonics have rapidly become an invaluable tool for information processing. Light-based computing accelerators are promising for boosting neural-network learning and inference [X. Xiao et al., APL Photonics 6, 126107 (2021); X. Meng et al., Nat. Commun. 14, 3000 (2023); J. Cheng et al., Nat. Commun. 15, 6189 (2024)] and optical interconnects are foreseen as a solution to the information transfer bottleneck in high-performance computing [Y. Li et al., in 2021 58th ACM/IEEE Design Automation Conference (DAC) (IEEE, 2021), pp. 931–936; A. Rizzo et al., IEEE J. Sel. Top. Quantum Electron. 29, 1 (2022); A. Netherton et al., Photonics Res. 12, A69 (2024)]. In this study, we demonstrate the successful programming of a transformation implemented using a reconfigurable photonic circuit with a nonconventional architecture. The core of most photonic processors is an MZI-based architecture [M. Reck et al., Phys. Rev. Lett. 73, 58 (1994); W. R. Clements et al., Optica 3, 1460 (2016)] that establishes an analytical connection between controllable parameters and circuit transformation. However, several architectures [R. Tang et al., IEEE Photonics Technol. Lett. 29, 971 (2017); M. Y. Saygin et al., Phys. Rev. Lett. 124, 010501 (2020)] that are substantially more difficult to program have improved robustness to fabrication defects. We use two algorithms that rely on different initial datasets to reconstruct the circuit model of a complex interferometer, and then program the required unitary transformation. The first method is based on the global fitting of the experimental calibration data, while the second method is an ML-based approach introduced in Kuzmin et al. [Opt. Express 29, 38429 (2021)]. Both methods performed accurate circuit programming with an average fidelity greater than 99% and 97%, respectively. Our results provide a strong foundation for the introduction of nonconventional interferometric architectures for photonic information processing.

Noninvasive localization of brain activity remains a fundamental challenge in neuroscience and clinical medicine. Magnetoencephalography source imaging (MSI) offers high spatiotemporal resolution but is constrained by its reliance on magnetically shielded environments. We propose a source-imaging approach using self-developed scalar optically pumped magnetometers that can operate under Earth’s ambient magnetic field without shielding. Through numerical simulations, we systematically assess how geomagnetic fields—particularly field orientation and gradients—impact localization accuracy, and compare these results with conventional shielded MSI. Our findings demonstrate that, under optimized conditions, the proposed method achieves comparable localization accuracy to traditional systems, which are performed in shielded environments. We also explore the potential advantages of unshielded MSI in enhancing spatial resolution. This work provides a theoretical basis for unshielded MSI and opens avenues for practical quantum sensing technologies in neuroscience and clinical applications.

Quantum optimization solvers typically rely on one-variable-to-one-qubit mapping. However, the low qubit count of current quantum computers is a major obstacle to competing against classical methods. Here, we develop a qubit-efficient algorithm that overcomes this limitation by mapping a candidate bit-string solution to an entangled wave function of fewer qubits. We propose a variational quantum circuit generalizing the quantum approximate optimization ansatz. Extremizing the ansatz for Sherrington-Kirkpatrick spin-glass problems, we show valuable properties such as the concentration of ansatz parameters and derive performance guarantees. This approach could benefit near-term intermediate-scale and future fault-tolerant small-scale quantum devices.

The effect of spin-orbit coupling in a Josephson diode has not been elucidated due to its interplay with the complexity of Josephson devices. Here, we systematically control local electric fields in epitaxial $\mathrm{Al}$-$\mathrm{In}\mathrm{As}$ Josephson junctions under in-plane magnetic fields and observe a polarity reversal of the Josephson diode. We interpret this polarity reversal as an effect of field-tunable spin-orbit coupling on nonreciprocal Josephson currents. A theoretical model, accounting for Rashba and Dresselhaus spin-orbit couplings in a planar Josephson junction containing many transverse sub-bands, aligns with the observed polarity reversal and its dependence on magnetic field. Our finding addresses spin-orbit control in a Josephson diode, enabling manipulation of Josephson harmonics.

The Mølmer-Sørensen (MS) gate is a two-qubit controlled-phase gate in ion traps that is highly valued due to its ability to preserve the motional state of the ions. However, its fidelity is obstructed by errors affecting the motion of the ions as well as the rotation of the qubits. In this work, we propose an amplitude-modulated composite MS gate, which features high fidelity, robust to gate timing, detuning, and coupling errors and is also tolerant of ac Stark shifts and drifting detuning errors. With the proposed amplitude-modulated composite sequence, we maintain gate infidelities below ${10}^{-3}$ even under simultaneous detuning, timing, and coupling errors as large as 10–15%, outperforming both standard and multitone MS gate implementations. The physical origin of this improvement is due to a separation of the error channels: the amplitude modulation suppresses residual spin-motion displacement (hence reducing sensitivity to detuning and timing imperfections), while the composite phase schedule cancels the dominant over- and under-rotation of the entangling phase produced by coupling miscalibration.