A nonadiabatic geometric quantum gate is realized by integrating nonadiabatic geometric phases with global geometric features into the unitary quantum control, thereby removing the limitation of a long evolution time in the adiabatic case. However, systematic errors are inevitable in practical quantum control; these lead to the deviation of the evolution from target conditions to inducing geometric phases, smearing the robustness of the induced geometric quantum gates. Here, we present a general theoretical framework with enhanced robustness for geometric quantum gates by preserving fundamental geometric conditions. We first analytically evaluate the influence of systematic errors on geometric gates and then propose an optimized approach to mitigate this influence. Numerical simulations indicate that, as the geometric conditions are still maintained in the presence of systematic errors in our scheme, the constructed geometric quantum gates exhibit strong robustness, far superior to that of conventional schemes. Furthermore, we propose implementing the scheme in superconducting quantum circuits, where geometric quantum gates can achieve high fidelity with current experimental parameters. Therefore, the enhanced gate performance highlights the promise of our scheme for large-scale quantum computations.

Nanomechanical resonator arrays constitute a promising platform for topological physics and integrated acoustic devices. However, achieving precise control of the couplings between resonators has been a significant challenge for realizing a time-dependent Hamiltonian. In this work, we address this challenge by designing the geometric parameters of the resonators, enabling us to achieve dynamical control over the coupling strength and frequency stability. Our scalable resonator array allows for the dynamic control of coupling strengths between every individual resonator pair, ranging from zero to more than 20 times the dissipation rate. Moreover, we demonstrate Rabi-like oscillations with real-time-varying Rabi frequencies. This dynamically controlled system provides an extended platform for investigating dynamic processes and their applications.

Tunable couplers in superconducting quantum computers have enabled fast and accurate two-qubit gates, with reported high fidelities over 99% in various architectures and gate-implementation schemes. However, there are few tunable couplers whose performance in multiqubit systems is clarified, except for the most widely used one: single-transmon coupler (STC). Achieving similar accuracy to isolated two-qubit systems remains challenging due to various undesirable couplings but is necessary for scalability. In this work, we numerically analyze a system of three fixed-frequency qubits coupled via two double-transmon couplers (DTCs) where nearest-neighbor qubits are highly detuned and also next-nearest-neighbor ones are nearly resonant. The DTC is a recently proposed tunable coupler, which consists of two fixed-frequency transmons coupled through a common loop with an additional Josephson junction. We find that the DTC cannot only reduce undesired residual couplings sufficiently, as well as in isolated two-qubit systems, but also enables implementations of 30-ns $\mathrm{CZ}$ gates and individual and simultaneous 10-ns $\pi /2$ pulses with fidelities over 99.99%. For comparison, we also investigate the system where the DTCs are replaced by the STCs. The results show that the DTC outperforms the STC in terms of both residual coupling suppression and gate accuracy in the above systems. From these results, we expect that the DTC architecture is promising for realizing high-performance, scalable superconducting quantum computers.

Quantum key distribution (QKD) could help to share a secure key between two distant peers. In recent years, twin-field (TF) QKD has been widely investigated because of its long transmission distance. One of the popular variants of TF QKD is sending-or-not-sending (SNS) QKD, which has been experimentally verified to realize 1000-km level fiber key distribution. In this article, we introduce phase postselection into the SNS protocol. With this modification, the probability of selecting “sending” can be substantially improved. The numerical simulation shows that the transmission distance can be improved both with and without the actively odd-parity pairing method. The case of discrete phase randomization is also analyzed, a similar improvement on distance can be seen.

In recent years, notable progress has been made in the study of hole qubits in planar germanium, and circuit quantum electrodynamics (circuit QED) has emerged as a promising approach for achieving long-range coupling and scaling up of qubits. Here, we demonstrate the coupling between holes in a planar germanium double quantum dot (DQD) and photons in a microwave cavity. Specifically, a real-time calibrated virtual gate method is developed to characterize this hybrid system, which in turn allows us to determine the typical parameters sequentially through a single-parameter fitting instead of conventional multiparameter fitting with additional uncertainty, giving a hole-photon coupling rate of $g_0$/2π = 21.7 MHz. This work is a step toward further research on hole-photon interactions and long-range qubit coupling in planar germanium. The experimental method developed in this work contributes to a more accurate and efficient characterization of hybrid cavity-QED systems.

Coupled learning is a contrastive local learning scheme for tuning the properties of individual elements within a network to achieve desired functionality of the system. It takes advantage of physics both to learn using local rules and to “compute” the output response to input data, thus enabling the system to perform decentralized computation without the need for a processor or external memory. We present three proof-of-concept mechanical networks of increasing complexity, and demonstrate how they can learn tasks such as self-symmetrization and node allostery via iterative tuning of individual spring rest lengths. These mechanical networks could feasibly be scaled and automated to solve increasingly complex tasks, hinting at a new class of smart metamaterials.

Superconducting transition-edge sensors (TESs) are a type of quantum sensor known for their high single-photon detection efficiency and low background. This makes TESs ideal for particle-physics experiments searching for rare events. In this work, we present a comprehensive characterization of the background in optical TESs, distinguishing three types of events: electrical-noise, high-energy, and photonlike events. We introduce computational methods to automate the classification of events. We experimentally verify and simulate the source of the high-energy events. We also isolate the photonlike events, the expected signal in dielectric haloscopes searching for dark-matter dark photons, and achieve a photonlike dark-count rate of 3.6×${10}^{-4}$ in the 0.8–3.2 eV energy range.

We study the dynamics of an optoelectronic circuit composed of an excitable nanoscale resonant-tunneling diode (RTD) driving a nanolaser diode (LD) coupled via time-delayed feedback. Using a combination of numerical path-continuation methods and time simulations, we demonstrate that this RTD-LD system can serve as an artificial neuron, generating pulses in the form of temporal localized states (TLSs) that can be employed as memory for neuromorphic computing. In particular, our findings reveal that the prototypical delayed FitzHugh-Nagumo model previously employed to model the RTD-LD resembles our more realistic model only in the limit of a slow RTD. We show that the RTD time scale plays a critical role in memory capacity as it governs a shift in pulse interaction from repulsive to attractive, leading to a transition from stable to unstable multipulse TLSs. Our theoretical analysis uncovers features and challenges previously unknown for the RTD-LD system, including the multistability of TLSs and attractive interaction forces, stemming from the previously neglected intrinsic dynamics of the laser. These effects are crucial to consider since they define the memory properties of the RTD-LD.

A reconstruction-free anatomical imaging algorithm that localizes the Compton scattering of annihilation $\gamma$ rays within an object using ultrafast time-of-flight information and provides an anatomical cross-section image without tomographic reconstruction has been previously proposed. However, the spatial performance of generated images remains unclear. This study introduces a theoretical understanding and supporting simulation results of a Compton scattering-based reconstruction-free anatomical imaging method. First, we provide the exact algebraic solution, which was solved approximately in a previous study. Using the algebraic solution, the system performance can be quantitatively estimated via total differentiation. Total differentiation indicated that the difference in the path lengths of annihilation $\gamma$ rays, which can be theoretically interpreted as the Compton scattering angle in an object strongly impacting the spatial resolution of the anatomical image. Monte Carlo simulations demonstrated that the spatial resolution of the generated images is improved by selecting events with large Compton scattering angles, which is consistent with the theory derived based on the algebraic solution. Furthermore, the use of energy information helped enhance the image quality by eliminating multiple scattering events in the imaging object.

This paper proposes the concept and demonstrates the realization of acoustic logic networks (ALNs), a system that performs various logic operations using sound waves. Unlike traditional systems with separate components, ALNs integrate multiple logic functionalities with a single platform, performing operations like and, or, and xor gates, similar to those used in electronic computers. The design of ALNs leverages the non-Hermitian and nonlocal metagratings. By considering the intricate nonlocal interaction within the structure, the metagratings are designed to operate at exceptional points (EPs) to achieve the desired logic functions. Two metagratings are cascaded to construct the acoustic logic module (ALM), which acts as the basic building block of an ALN. The effectiveness of ALNs is confirmed through theoretical calculations, numerical simulations, and experiments. We demonstrate the ability to perform complex logic operations and realize various multichannel computing functions with the ALN of different configurations. These results showcase precise control over sound waves and significantly enhanced computing capabilities. The proposed ALNs represent a significant step forward in acoustic computing. This technology has the potential to revolutionize fields where traditional electronic computing might be impractical due to size, energy efficiency, or environmental limitations.

Cold-atom magnetometers exploit a dense ensemble of quanta with long coherence times to realize leading sensitivity on the micrometer scale. Configured as a Ramsey interferometer, a cold-atom sensor can approach atom shot-noise limited precision but suffers from fringe ambiguity, producing gross errors when the field falls outside a narrow predefined range. We describe how Hilbert-demodulated optical magnetometry can be realized on cold-atom sensors to provide field measurements both precise and unambiguous. Continuous reconstruction of the Larmor phase allows us to determine the dc magnetic field unambiguously in an unshielded environment, as well as measure ac variation of the field, in a single shot. The ac measurement allows us to characterize, and then neutralize, line-synchronous magnetic interference, extending reconstruction times. Using $1.6\times {10}^6{}^{87}\mathrm{Rb}$ atoms in a volume of $(68\text{μ}\mathrm{m}{)}^3$, we measure a test field to be $86.0121261(4)\text{μ}\mathrm{T}$ in a single shot, achieving dc sensitivity of $380\mathrm{fT}$ in a duration of $1000\mathrm{ms}$. Our results demonstrate that Hilbert-demodulated optical readout yields metrologically significant sensitivity without the fringe ambiguity inherent to Ramsey interferometry.

Increased demand for high-performance permanent magnets in the electric vehicle and wind-turbine industries has prompted the search for cost-effective alternatives. Discovering magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge to researchers because of issues with the global supply of rare-earth elements, material stability, and a low maximum magnetic energy product $B{H}_{\text{max}}$. While first-principles density functional theory (DFT) predicts materials’ magnetic moments, magnetocrystalline anisotropy constants, and exchange interactions, it cannot compute extrinsic properties such as coercivity ($H_c$). Although it is possible to calculate $H_c$ theoretically with micromagnetic simulations, the predicted value is larger than the experiment by almost an order of magnitude due to the Brown paradox. To circumvent these issues, we employ machine-learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. The use of a large experimental dataset enables realistic $H_c$ predictions for materials such as $\text{Ce}$-doped ${\text{Nd}}_2{\text{Fe}}_{14}\text{B}$, comparing favorably against micromagnetically simulated coercivities. Remarkably, our ML model accurately identifies uniaxial magneto-crystalline anisotropy as the primary contributor to $H_c$. With DFT calculations, we predict the $\mathrm{Nd}$-site-dependent magnetic anisotropy behavior in ${\text{Nd}}_2{\text{Fe}}_{14}\text{B}$, confirming that $\text{Nd}4g$ sites mainly contribute to uniaxial magnetocrystalline anisotropy, and also calculate the Curie temperature ($T_{\mathrm{c}}$). Both calculated results are in good agreement with the experiments. The coupled experimental dataset and ML modeling with DFT input predict $H_c$ with far greater accuracy and speed than was previously possible using micromagnetic modeling. Further, we reverse engineer the grain-boundary and intergrain exchange coupling with micromagnetic simulations by employing the ML predictions.

Resonant states underpin a variety of metastructures that exhibit remarkable capabilities for the effective control of acoustic waves at subwavelength scales. The development of metamaterials relies on rigorous mode engineering, providing the implementation of the desired properties. At the same time, the application of metamaterials is still limited, as their building blocks are frequently characterized by complicated geometry and cannot be tuned easily. In this work, we consider a simple system of coupled Helmholtz resonators and study their properties associated with the tuning of coupling strength and symmetry breaking. We numerically and experimentally demonstrate the excitation of the quasibound state in the continuum in the resonators placed in free space and in a rectangular cavity. It is also shown that tuning the intrinsic losses via introducing porous inserts can lead to spectral splitting or merging of quasibound states in the continuum and the occurrence of exceptional points. The obtained results open opportunities for the development of simple and easily tunable metastructures based on Helmholtz resonances.

A crucial requirement for quantum computing—in particular, for scalable quantum computing and error correction—is fast and high-fidelity qubit readout. For semiconductor-based qubits, one limiting factor for local low-power signal amplification is the output swing of the charge sensor. We demonstrate $\mathrm{Ga}\mathrm{As}$ and Si asymmetric sensing dots (ASDs) specifically designed to provide a significantly larger response compared to conventional charge-sensing dots. Our ASD design features a drain reservoir strongly decoupled from the sensor dot, which mitigates negative feedback effects found in conventional sensors. This results in a boosted output swing of $3\mathrm{mV}$, which exceeds the response in the conventional regime of our device by more than a factor of 10. The enhanced output signal paves the way for employing very low-power readout amplifiers in close proximity to the qubit.

Nitrogen-vacancy ($\mathrm{N}$-$V$) centers in diamond enable nuclear magnetic resonance (NMR) spectroscopy of samples at the nano- and microscale. However, at typical tesla-scale NMR magnetic field strengths, $\mathrm{N}$-$V$–NMR protocols become difficult to implement due to the challenge of driving fast $\mathrm{N}$-$V$ pulse sequences sensitive to nuclear Larmor frequencies above a few megahertz. We perform simulations and theoretical analysis of the experimental viability of $\mathrm{N}$-$V$ NMR at tesla-scale magnetic fields using a measurement protocol called DRACAERIS (Double Rewound ACquisition Amplitude Encoded Radio Induced Signal). DRACAERIS detects the longitudinal magnetization of the NMR sample at a much lower driven Rabi frequency, more suitable technically for $\mathrm{N}$-$V$ detection. We discuss how pulse errors, finite pulse lengths, and nuclear spin-spin couplings affect the resulting NMR spectra. We find that DRACAERIS is less susceptible to pulse imperfections and off-resonance effects than previous protocols for longitudinal magnetization detection. We also identify reasonable parameters for experimental implementation.