Interest in quantum machine learning is growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use of quantum machine-learning techniques for image-classification tasks. We exploit a quantum extreme learning machine by taking advantage of its rich feature map provided by the quantum reservoir substrate. We systematically analyze different phases of the quantum extreme learning machine process, from dataset preparation to final image classification. In particular, we test different encodings, together with principal component analysis and the use of autoencoders, and we examine the dynamics of the model through the use of different Hamiltonians for the quantum reservoir. Our results show that the introduction of a quantum reservoir systematically improves the accuracy of the classifier. In addition, our findings indicate that variations in encoding methods can significantly influence performance and that Hamiltonians with distinct structures exhibit the same discrimination rate, depending on how their eigenstates are related to the encoding and measurement basis.

Materials imperfections in $\mathrm{Nb}$-based superconducting quantum circuits—in particular, two-level-system (TLS) defects—are a major source of decoherence, ultimately limiting the performance of quantum computation and sensing. Thus, identifying and understanding the microscopic origin of possible TLS defects in these devices will help develop strategies that eliminate them, which are key to superconducting qubit performance improvement. In this paper, we demonstrate an order-of-magnitude reduction in two-level system losses in three-dimensional superconducting radio frequency (SRF) niobium resonators by a 10-h high vacuum (HV) heat treatment at 650 °C, even after exposure to air and high-pressure rinsing (HPR). X-ray photoelectron spectroscopy (XPS) and high-resolution scanning transmission electron microscopy (STEM) reveal an alteration of the native oxide composition regrown after air exposure and HPR and the creation of nanoscale crystalline oxide regions, which correlates with the measured tenfold quality factor enhancement at low fields of the 1.3 GHz niobium resonator. Tunneling spectroscopy measurements show a pronounced proximity effect that further confirms the presence of metallic layers on the niobium surface.

Spin-gapless semiconductors (SGSs) are emerging as a promising class of materials for spintronic applications, offering unique opportunities to realize functionalities beyond conventional electronics. In this work, we propose a concept of multifunctional spintronic field-effect transistors (FETs) using SGSs and/or spin-gapped metals (SGMs) as source and drain electrodes. These devices operate similarly to metal-semiconductor Schottky-barrier FETs, where a potential barrier forms between the SGS (or SGM) electrode and the intrinsic semiconducting channel; however, unlike conventional Schottky-barrier FETs, our proposed devices exploit the distinctive spin-dependent transport properties of SGS and SGM electrodes to achieve sub-60-mV/dec switching, significantly surpassing the 60 mV/dec subthreshold swing (SS) limit in traditional MOSFETs, thereby enabling low-voltage operation. Additionally, the proposed FETs exhibit a nonlocal giant magnetoresistance (GMR) effect, enhancing functionality by enabling nonvolatile memory capabilities. The incorporation of SGMs also introduces a negative differential resistance effect with an ultrahigh peak-to-valley current ratio, further expanding the device’s multifunctionality. Two-dimensional (2D) nanomaterials provide a promising platform for realizing these advanced FETs. We perform a comprehensive screening of the computational 2D materials database to identify suitable SGS and SGM candidates. Among the materials identified, several exhibit Curie temperatures significantly above room temperature, ensuring robust ferromagnetic properties for practical applications. For device simulations, we select $\mathrm{VS}$${}_2$ as the SGS material and, as a proof of concept, employ a nonequilibrium Green’s function method combined with density functional theory to simulate the transfer ($I_{\mathrm{D}}$-$V_{\mathrm{G}}$) and output ($I_{\mathrm{D}}$-$V_{\mathrm{D}}$) characteristics of a vertical $\mathrm{VS}$${}_2$/$\mathrm{Ga}$${}_2$$\mathrm{O}$${}_2$ heterojunction FET. Our calculations predict a remarkably low SS of 20 mV/dec, a high on-off ratio of ${10}^8$, and a significant nonlocal GMR effect, demonstrating the potential of these devices for low-power, high-performance logic and memory applications.

A squeezed state is one of the most vital quantum resources in quantum information processing. The extensive attention has increasingly shifted to not only the squeezed degree but also the squeezed bandwidth in quantum precise measurement. Here, we demonstrate the generation of a bandwidth-tunable squeezed state by exploiting the entangled sideband modes and the postprocessing scheme, with the quantum noise reduction of $8.0\pm 0.3$ dB across a wide frequency range from 10 Hz to 10 MHz. Moreover, the bandwidth and rotation frequency can be conveniently manipulated in the whole frequency range according to the application requirement. There is no doubt that the demonstration will open up some potential applications about squeezed-state interaction with atoms.

The anomalous Hall effect (AHE) in magnetic materials has attracted tremendous interest in condensed-matter physics. Recent studies reveal that the intrinsic contribution to the AHE would arise out of the Berry curvature (BC) of occupied electronic states. The BC is closely related to nontrivial band crossings such as Dirac and Weyl nodes or nodal lines. Here, we report the comprehensive electronic band structure and transport properties of the ${\mathrm{Nd}\mathrm{Mn}\mathrm{Si}}_2$ ferromagnet. A gapped nodal ring near the Fermi level ($E_{\mathrm{F}}$) induced by spin-orbit coupling was observed in the calculated energy band structure, which brings forth a large BC and accordingly leads to a large anomalous Hall conductivity (AHC). Within the permissible range of error, this theoretical value can be reproduced by experimental measurements to be approximately $192{\mathrm{\Omega }}^{-1}{\mathrm{cm}}^{-1}$. Our study explored the theoretical origin of the BC-induced AHE and provides experimental evidence for BC-induced large AHC in topological nodal-ring semimetal systems.

A novel way to represent and analyze the contours of laser-pulse-induced domains, which reflect the position at which the laser fluence drops below a certain threshold and are measured as a function of the laser-pulse energy, is introduced. It enables the visualization and quantitative characterization of arbitrarily shaped in situ laser spots with high accuracy.

We study the implementation of holonomic quantum gates preserved from arbitrary single-qubit and two-qubit decoherence or errors using a dynamical decoupling approach. To integrate the holonomic feature with the dynamical decoupling technique, we require only one auxiliary qubit for single-qubit gates and no auxiliary qubit for two-qubit gates. Our scheme uses far fewer physical qubits to achieve this integration compared to existing proposals in the literature. Moreover, the scheme is based on system interactions that can be experimentally realized with superconducting qubits. These advantages suggest that our scheme is a promising step toward developing noise-resistant quantum devices.

We introduce iQSync, a clock-offset recovery method designed for implementation on low-level hardware, such as field-programmable gate arrays or microcontrollers, for quantum key distribution (QKD). iQSync requires minimal memory, only a simple instruction set (e.g., no floating-point operations), and can be evaluated with sublinear time complexity, typically involving no more than a few thousand iterations of a simple loop. Furthermore, iQSync allows for a precise clock-offset recovery within a few seconds, even for large offsets, and is well suited for scenarios with high channel loss and low signal-to-noise ratio, irrespective of the prepare-and-measure QKD protocol used. We implemented the method on our QKD platform, demonstrating its performance and conformity with analytically derived success probabilities for channel attenuations exceeding $70\mathrm{dB}$.

We present an exact $n$-qubit computational-basis amplitude encoder of real- or complex-valued data vectors of $d=(\genfrac{}{}{0ex}{}{n}{k})$ components into a subspace of fixed Hamming weight $k$. This represents a polynomial space compression of degree $k$. The circuit is optimal in that it expresses an arbitrary data vector using only $d-1$ (controlled) reconfigurable beam splitter (RBS) gates. It is constructed by an efficient classical algorithm that sequentially generates all bitstrings of weight $k$ and identifies the gates that superpose the corresponding states with the correct amplitudes. An explicit compilation into $\mathrm{CNOT}\mathrm{s}$ and single-qubit gates is presented, with the total $\mathrm{CNOT}$-gate count of $\mathcal{O}(kd)$ provided in analytical form. In addition, we show how to load data in the binary basis by sequentially stacking encoders of different Hamming weights using $\mathcal{O}(d\log (d))\mathrm{CNOT}$ gates. Moreover, using generalized RBS gates that mix states of different Hamming weights, we extend the construction to efficiently encode arbitrary sparse vectors. We perform a proof-of-principle demonstration of our scheme on a commercial trapped-ion quantum computer. We successfully upload a $q$-Gaussian probability distribution in the non-log-concave regime with $n=6$ and $k=2$. We also illustrate how the effect of hardware noise can be alleviated by quantum error mitigation. Numerically, we show how our encoder can improve the performance of variational quantum algorithms for problems that include particle-preserving symmetries. Our results constitute a versatile framework for quantum data compression with various potential applications in fields such as quantum chemistry, quantum machine learning, and constrained combinatorial optimizations.

This study investigates the performance of a binarized neuromorphic network leveraging polariton dyads, optically excited pairs of interfering polariton condensates within a microcavity to function as binary logic gate neurons. Employing numerical simulations, we explore various neuron configurations, both linear (nand, nor) and nonlinear (xnor), to assess their effectiveness in image classification tasks. We demonstrate that structural nonlinearity, derived from the network’s layout, plays a crucial role in facilitating complex computational tasks, effectively reducing the reliance on the inherent nonlinearity of individual neurons. Our findings suggest that the network’s configuration and the interaction among its elements can emulate the benefits of nonlinearity, thus potentially simplifying the design and manufacturing of neuromorphic systems and enhancing their scalability. This shift in focus from individual neuron properties to network architecture could lead to significant advancements in the efficiency and applicability of neuromorphic computing.

Spatial imaging of magnetic stray fields from magnetic materials is a useful tool for identifying a material’s underlying magnetic configurations; however, transforming a magnetic image into a magnetization image is an ill-posed problem, and this can result in artefacts that limit the inferences that can be drawn from the material under investigation. In this work, we develop a neural-network fitting approach that approximates this transformation, reducing these artefacts. In addition, we demonstrate that the approach allows the inclusion of additional models and bounds that are not possible with traditional reconstruction methods. These advantages allow for the reconstruction of nontrivial magnetization textures with varying magnetization directions in thin-film magnets, which was not possible previously. We demonstrate this capability by performing magnetization reconstructions on a variety of topological spin textures.

We demonstrate efficient in-plane optical fiber collection of single-photon emission from a quantum dot embedded in a photonic-crystal cavity. This was achieved via adiabatic coupling between a tapered optical fiber and a tapered on-chip photonic waveguide coupled to a modified high-$Q$ L3 photonic cavity, with a theoretical in-plane fiber-collection efficiency of 47%. Experimentally, this efficiency is measured by comparing the luminescence intensities collected by the fiber and by a NA 0.65 microscope objective placed above the cavity and reaches half of the theoretically expected value. Simulations show that the in-plane collection efficiency can be further increased to 77% by improving the dot-cavity coupling and by increasing the overlap length between the tapered fiber and waveguide. Near-unity adiabatic coupling is predicted for an overlap length of $5\text{μ}\mathrm{m}$. The single-photon source was characterized by second-order correlation measurements giving $g^{(2)}(0)=0.17$ under nonresonant excitation.

Geometric quantum machine learning uses the symmetries inherent in data to design tailored machine learning tasks with reduced search space dimension. The field has been well studied recently in an effort to avoid barren plateau issues while improving the accuracy of quantum machine learning models. This work explores the related problem of learning an equivariant map given two unitary representations of a finite group, which in turn allows the symmetric embedding of the data to be learned rather than simply required. Moreover, this procedure allows the learning of covariant quantum channels, which are an essential tool in quantum information theory. We demonstrate the feasibility of this task and give examples to illustrate the procedure.

In this paper, we adopt $\chi$-type states to design an alternative circular semiquantum private comparison (SQPC) protocol, which can determine the equality of private inputs from two semiquantum users within one round implementation under the help of a semihonest third party (TP) who possesses complete quantum capabilities. Here, it is assumed that the semihonest TP has the abilities to launch all possible attacks to steal useful information about two semiquantum users’ private inputs but cannot conspire with anyone else. The traveling particles go from TP to Alice, then from Alice to Bob, and back from Bob to TP. The security analysis turns out the proposed SQPC protocol can resist both the outside attacks and the inside attacks. Furthermore, it has been proven to be information-theoretically secure in the asymptotic scenario. Compared with some existing SQPC protocols of equality with quantum entangled states, the proposed SQPC protocol has some advantages: $◯1$ it requires no preshared key among different participants; $◯2$ it does not need quantum entanglement swapping; $◯3$ it employs no delay lines; $◯4$ it adopts no unitary operations; and $◯5$ it has fewer qubits transmitted and fewer classical modulo 2 addition computations.

The discovery of higher-order topological insulator metamaterials has enriched fundamental schemes of classical wave manipulations. A common way of inducing higher-order topological wave phenomena is based on the original $C_n$-symmetric lattice characterized by a quantized Wannier center, which generally supports the higher-order topological states in a single band gap. Here, we report the acoustic realization of dual-band second-order topological insulators in $C_{4v}$-symmetric composite lattices, created by inserting extra sites into each inversion center of four adjacent unit cells of the original two-dimensional Su-Schrieffer-Heeger lattice. Due to the hybrid operation, we can create distinct nontrivial boundaries along the inserted sites, and multiple acoustic localized states occur in two band gaps simultaneously. Relying on two square-shaped sonic crystals with nontrivial boundaries, we experimentally verify the diverse state localizations on the corners and edges of the sonic crystals, and distinct phase profiles are observed for the boundary states in different band gaps. The topological robustness of corner and edge states under bulk disorder is experimentally demonstrated in comparison with the measured spectra of relevant boundary disorders. Our work opens an avenue to develop multifrequency acoustic devices, which may facilitate useful applications of multiband acoustic sensors and energy trapping.