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Geometrically local quantum codes, comprised of qubits and checks embedded in R D with local check operators, have been a subject of significant interest. A key challenge is identifying the optimal code construction that maximizes both code dimension and distance under the geometric constraints. In this work, we introduce a construction that can transform any good quantum LDPC code into an almost optimal geometrically local quantum code. Our approach hinges on a novel yet simple procedure that extracts a two-dimensional structure from an arbitrary three-term chain complex, building a connection between geometric operations and code constructions. We expect that this procedure will find broader applications in areas such as weight reduction and the geometric realization of chain complexes.
Three-dimensional integration is a pivotal strategy for advancing the integrated circuit performance beyond traditional scaling limits. Herein, we report a monolithic 3D (M3D) integration technology using carbon nanotube (CNT) networks to fabricate four-layer complementary metal oxide semiconductor (CMOS) thin-film transistors (TFTs), representing the highest layer count to date for CNT-based CMOS M3D devices. The vertically stacked structure achieves enhanced integration density through layer-by-layer stacking with interlayer isolation via low-temperature-processed dielectrics, enabling the stable operation of N-type and P-type CNT-TFTs in separate layers. Functional IC units, including CMOS inverters and trans-impedance amplifiers (TIAs), were successfully implemented using this four-layer architecture. Furthermore, vertical integration of the M3D TIA with a molybdenum disulfide (MoS2) photodetector realized a monolithic optoelectronic sensing system, achieving primary amplification of photocurrents with a trans-impedance gain of 7.21 × 103 Ω. This work validates the feasibility of CNT-based M3D integration for high-density, multifunctional ICs and paves the way for next-generation optoelectronic systems and power devices.
Pure-green formamidinium lead bromide (FAPbBr3) perovskite quantum dots (PQDs) are particularly attractive for display and lighting applications. However, their inherent instability and processing challenges hinder their widespread application and commercialization. The instability of PQDs under exposure to light, heat, water, and oxygen is primarily attributed to their low formation energy, leading to phase transformations, agglomeration, and degradation, which negatively impact their optical properties. To address these challenges, this study proposes a dual-interface encapsulation strategy that integrates inorganic-organic synergy and covalent surface coupling into a single hierarchical framework. In this work, we present a cost-effective hierarchical multicoating strategy for stabilizing pure-green FAPbBr3 PQDs using industrially accessible stabilization agents, namely SiOx and dicyclopentanyl methacrylate (513M). Specifically, this research utilizes (3-aminopropyl) triethoxysilane (APTES) as a coupling agent ligand and tetraethoxysilane to uniformly coat the PQDs by SiOx. Following this, 513M, a monomer, is radically polymerized on the surface of the SiOx-coated PQDs to form a secondary shell layer. The initial coating enhances the PQDs' resistance to environmental factors, while the secondary layer (a hydrophobic polymer) further improves environmental stability without compromising the PQDs' structure during polymerization. The resulting FAPbBr3@SiOx@513M composite material, resulted in powder form, significantly improves the PQDs' durability against environmental conditions while maintaining excellent optical properties, including emission at ∼532 nm, a full width at half-maximum of ≤28 nm, and a photoluminescence quantum yield of >50%, demonstrating that robust environmental protection can be achieved without relying on record-high optical parameters or costly materials. Owing to its use of low-cost, scalable materials and pure-green emissive PQDs, this multicoating strategy offers a realistic pathway toward industrially viable, solid-state PQD materials for optoelectronic applications.
The accelerating global economic competition and the rapid development of intelligent technologies present both new opportunities and challenges for enterprises. Intelligent transformation has become an imperative trend for enhancing competitiveness, yet Chinese enterprises are still in the preliminary stages. Focusing on the supply-side (intelligent server providers) and the demand-side (adopting enterprises), this study develops a two-layer heterogeneous complex network model grounded in complex network and evolutionary game theories. We analyze the dynamic evolutionary mechanisms and key influencing factors of strategic choices for both types of firms under different scenarios. Python-based simulations reveal that increased government subsidies, reduced intelligent server costs, higher additional benefits from transformation, and appropriate pricing strategies all promote evolutionary cooperation between the two sides. Furthermore, the network structure significantly impacts strategic selection. The model's parameters are calibrated using 2023 financial data from Foxconn Industrial Internet Co., Ltd. to anchor the simulation in a representative large-enterprise scenario. This research extends the study of intelligent transformation from a static perspective to a dynamic, spatial-relationship-aware view, and addresses the limitation of participant homogeneity by employing a two-layer heterogeneous network model, thereby providing theoretical support and context-specific insights for enterprise intelligent transformation.
Taiwan's strategic focus in digital healthcare has been officially integrated into national industrial policy and identified as a crucial application area for artificial intelligence (AI) and next-generation communication technologies. As the healthcare sector undergoes rapid digital transformation, digital healthcare technologies have emerged as essential tools for improving medical quality and efficiency. Leveraging the extensive coverage of its National Health Insurance (NHI) system and its strengths in Information and Communications Technology (ICT), Taiwan also benefits from the robust research capacity of universities and hospitals. Government-driven regulatory reforms and infrastructure initiatives are further accelerating the advancement of the NHI MediCloud system and the broader digital healthcare ecosystem. This article provides a comprehensive overview of smart healthcare development, highlighting government policy support and the R&D capabilities of universities, research institutes, and hospitals. It also examines the ICT industry's participation in the development of smart healthcare ecosystems, such as Foxconn, Quanta, Acer, ASUS, Wistron, Qisda, etc. With strong data assets, technological expertise, and policy backing, Taiwan demonstrates significant potential in both AI innovation and smart healthcare applications, steadily positioning itself as a key player in the global healthcare market.
Artificial electronic skins that mimic the properties and functionality of human skin are becoming increasingly important for human-robot interactions. One ability of human skin yet to be thoroughly imitated in electronic skins is non-contact temperature sensing. Imitating this property will be useful for creating novel touch-free interfaces for human-centered robotic systems. Ionic-conducting sensing layers made from crosslinked pectin films have recently been found to exhibit extremely high contact temperature sensitivity, several orders of magnitude greater than traditional sensors. However, pectin film sensors suffer from large baseline conductance decays during prolonged measurements, and their non-contact thermal radiation sensing capabilities have not yet been systematically investigated. Here, substantially improved thermal radiation iontronic sensing stability is first demonstrated by implementing an alternating current configuration with the pectin films. The performance of various polymeric coatings is additionally studied for preventing dehydration in pectin films to improve prolonged iontronic sensor performance. It is then shown that the pectin film sensors exhibit non-contact temperature sensing response that closely match analytical models for radiative heat transfer rate. Altogether, the findings demonstrate clear advances toward non-contact temperature-based electronic skins and touch-free interfaces from pectin or other ionic-conducting films.
The chiral edge current is the boundary manifestation of the Chern number of a quantum anomalous Hall (QAH) insulator. The van der Waals antiferromagnet MnBi2Te4 is theorized to be a QAH in odd-layers but has shown Hall resistivity below the quantization value at zero magnetic field. Here, we perform scanning superconducting quantum interference device (sSQUID) microscopy on these seemingly failed QAH insulators to image their current distribution. When gated to the charge neutral point, our device exhibits edge current, which flows unidirectionally on the odd-layer boundary both with vacuum and with the even-layers. The edge current chirality reverses with the magnetization of the bulk. Surprisingly, we find the edge channels coexist with finite bulk conduction even though the bulk chemical potential is in the band gap, suggesting their robustness under significant edge-bulk scattering. Our result establishes the existence of chiral edge currents in a topological antiferromagnet and offers an alternative for identifying QAH states.
1,3-Dioxolane (DOL), with its broad liquid phase temperature window and low Li+-solvent binding energy, stands out as an ideal solvent candidate for the wide-temperature and high-rate electrolytes. Unfortunately, DOL is susceptible to undergo ring-opening polymerization under common lithium salts, which markedly retards the reaction kinetics. This work introduces the organic basic additive 1,8-Diazabicyclo[5.4.0]undec-7-ene (DBU) to effectively suppress the polymerization, thus achieving compatibility between LiFSI, LiDFOB lithium salts, and DOL. Furthermore, density functional theory (DFT) calculations are utilized to elucidate the underlying mechanisms of DOL polymerization and to clarify how DBU inhibits its polymerization. The resulting electrolyte, devoid of polymer chain formation, forms a weak solvation structure rich in anions, which demonstrates rapid ion transport kinetics in the bulk electrolyte and excellent electrochemical stability at the electrolyte-electrode interfaces (EEIs) simultaneously. When applied to the LiFePO4||graphite full cell, it exhibits exceptional wide-temperature and high-rate performance, with specific capacities reaching 101.2 mAh g -1 at room temperature (20 C), 36.9 mAh g-1 at -40 °C (0.5 C), and 118.0 mAh g-1 at 60 °C (20 C). This study significantly guides the development of wide-temperature, high-rate electrolytes.
Two-dimensional (2D) semiconductors have been of great interest for phototransistors and neuromorphic devices in recent years because of their unique optical and electronic properties. However, the detectable spectral range and light absorption efficiency are limited for 2D-semiconductor-based phototransistors. Herein, we report a high-performance deep-ultraviolet (DUV) sensitive phototransistor by integrating molybdenum disulfide (MoS2) with silicon carbide nanoparticles (SiC NPs) to form a van der Waals heterostructure (vdWH), which shows ultrahigh responsivity and detectivity, especially in the DUV spectral range. The SiC NPs/few-layer MoS2 vdWH phototransistor shows a 20-fold enhancement in responsivity (from 9.4 × 102 to 1.9 × 104 A/W) and 11-fold enhancement in detectivity (from 7.9 × 1012 to 8.4 × 1013 cm × Hz1/2/W) at 254 nm wavelength, compared to the phototransistor based on few-layer MoS2 alone. Moreover, the SiC NPs/few-layer MoS2 vdWH phototransistor also shows higher excitation postsynaptic current (EPSC) and longer retention time of postsynaptic current (PSC) compared to the phototransistor based on few-layer MoS2 alone. This enables vdWH devices to successfully mimic various biological synaptic functions, including paired-pulse facilitation (PPF), spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-frequency-dependent plasticity, the transition from short-term plasticity (STP) to long-term plasticity (LTP), and long-term depression (LTD) capabilities. The simulation of a deep neural network (DNN) shows that the image inference accuracy based on these SiC NPs/few-layer MoS2 vdWH neuromorphic phototransistors reaches up to 98.99% even after considering the photoresponsivity variations. The high-performance dual-function neuromorphic optoelectronics based on SiC NPs/MoS2 vdWH hold great promise for ultrasensitive DUV photodetection, neuromorphic DUV visual sensing, and in-sensor computing applications in a single device.
In this paper, the fabrication and the corresponding performance characteristics of resonant cavity micro-light-emitting diodes (RC-μ-LEDs) are examined, with particular emphasis placed on reducing the light emission angle to enhance their application efficiency. A stepped quantum well structure and a multilayer aperture distributed Bragg reflector (DBR) are used to reduce the light emission angle, and two different approaches are investigated: one is by adding a multilayer DBR structure, and the other is by incorporating a microlens (ML) structure. The experimental results show that both adjusting the DBR cycles and adding microlenses can effectively reduce the dispersion angle of light emission, and thus improving the directionality of light, wavelength stability, and the overall device performance. Such highly directional light sources offer great solutions for optical communications, micro-LEDs, and augmented reality (AR) applications.
Entanglement is at the heart of quantum theory and is responsible for various quantum-enabling technologies. In practice, during its preparation, storage, and distribution to the intended recipients, this valuable quantum resource may suffer from noisy interactions that reduce its usefulness for the desired information-processing tasks. Conventional schemes of entanglement distillation aim to alleviate this problem by performing collective operations on multiple copies of these decohered states and sacrificing some of them to recover Bell pairs. However, for this scheme to work, the states to be distilled should already contain a large enough fraction of maximally entangled states before these collective operations. Not all entangled quantum states meet this premise. Here, by using the paradigmatic family of two-qutrit Werner states as an exemplifying example, we experimentally demonstrate how one may use single-copy local filtering operations to meet this requirement and to recover the quantumness hidden in these higher-dimensional states. Among others, our results provide the first proof-of-principle experimental certification of the Bell-nonlocal properties of these intriguing entangled states, the activation of their usefulness for quantum teleportation, dense coding, and an enhancement of their quantum steerability, and hence usefulness for certain discrimination tasks. Our theoretically established lower bounds on the steering robustness of these states, when they admit a symmetric quasiextension or a bosonic symmetric extension, and when they show hidden dense-codability, may also be of independent interest.
Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intensive task. Extensive expert annotations are needed to train effective machine learning models. This study presents an innovative approach that leverages federated learning in combination with a teacher‒student framework, an enhanced slice-aware network (SANet), and semisupervised learning (SSL) techniques to improve the CT-based liver tumor detection process while significantly reducing its labor and time costs. Federated learning enables collaborative model training to be performed across multiple institutions without sharing sensitive patient data, thus ensuring privacy and security. The teacher-student SANet framework takes advantage of both teacher and student models, with the teacher model providing reliable pseudolabels that guide the student model in a semisupervised manner. This method not only improves the accuracy of liver tumor detection but also reduces the dependence on extensively annotated datasets. The proposed method was validated through simulation experiments conducted in four scenarios, and it demonstrated a model accuracy of 83%, which represents an improvement over the original locally trained models. This study presents a promising method for enhancing the CT-based liver tumor detection while reducing the incurred labor and time costs by utilizing federated learning, the teacher-student SANet framework, and SSL techniques. Compared with previous approaches, the proposed method achieved a model accuracy of 83%, representing a significant improvement. Not applicable.
The vast and complicated many-qubit state space forbids us to comprehensively capture the dynamics of modern quantum computers via classical simulations or quantum tomography. Recent progress in quantum learning theory prompts a crucial question: can linear properties of a many-qubit circuit with d tunable RZ gates and G - d Clifford gates be efficiently learned from measurement data generated by varying classical inputs? In this work, we prove that the sample complexity scaling linearly in d is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. To address this challenge, we propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead. Our results advance two crucial realms in quantum computation: the exploration of quantum algorithms with practical utilities and learning-based quantum system certification. We conduct numerical simulations to validate our proposals across diverse scenarios, encompassing quantum information processing protocols, Hamiltonian simulation, and variational quantum algorithms up to 60 qubits.
By integrating super-aligned carbon nanotube (SACNT) films with paraffin wax, an addressable optical valve composite array was created through screen printing and laser cutting. The temperature of the SACNT film can be controlled, which rapidly induces phase changes in the paraffin wax, leading to a swift change in optical transparency. The transmission spot exhibited significant differences, with a contrast degree reaching up to 0.65. At a paraffin wax surface density of 1.17 × 10-4 g mm-2, the rise and fall times of the transmitted optical signal across the 350-1100 nm spectrum were 155 ± 2 ms and 135 ± 11 ms, respectively, enabling rapid spatial light modulation. A prototype was fabricated, capable of dynamically displaying letters, with the crosstalk effect of the current being significantly mitigated in spatial light modulation. This rapid spatial light modulation prototype can be customized to any shape and size, and it can either be freestanding or mounted on any substrate. This innovation offers a new approach to spatial light modulation.
Lithium-ion batteries are currently the mainstream for almost all portables, and quickly expand in electrical vehicles and grid storage applications. However, they are challenged by the poor safety regarding organic liquid electrolytes and relatively low energy density. Solid-state batteries, characterized by using solid-state electrolytes (SSEs), are recognized as the next-generation energy technology, owing to their intrinsically high safety and potentially superior energy density. However, developing SSEs is impeded by several key factors, including low ionic conductivity, interfacial issues, and high-cost in industrial scales. Recently, a novel category of SSEs, known as frameworked electrolytes (FEs), has emerged as a formidable contender for the transition to all-solid-state batteries. FEs exhibit a unique macroscopically solid-state nature and microscopically sub-nanochannels offering high ionic conductivity. In this perspective, the unique lithium-ion transport mechanisms within FEs are explored and 2D vertically conductive metal-organic framework (MOF) is proposed as an even more promising FE candidate. The abundant active sites in the 1D sub-nanochannels of 2D vertically conductive MOFs facilitate efficient ion transport, favorable interfacial compatibility, and scalable industrial applications. This perspective aims to boost the emergence of novel SSEs, promoting the realization of long-expected all-solid-state batteries and inspiring future energy storage solutions.
Contact engineering at the semiconductor-electrode and semiconductor-dielectric interfaces is critical to the performance of electronic devices, especially for delicate 2D semiconductors. Here, this study proposes a new paradigm of flexible field-effect transistors featuring solid-liquid hybrid interfaces, in which liquid metal and ionic liquid, confined within microchannels, function as the source/drain electrodes and gate dielectric, respectively. These interfaces provide MoS₂ with undisturbed, atomically smooth electrical contacts, and enable efficient gate control via electric double layers. Benefiting from the inherent softness of liquids and their damage-free processing, Fermi level pinning is significantly mitigated by the liquid metal, achieving a pinning factor |s|  =  0.7. Meanwhile, the ionic liquid enables a subthreshold swing of 60.7 mV dec-1, approaching the theoretical thermal limit. Furthermore, our flexible transistors demonstrate multifunctionality as enhanced logic gates, low-voltage inverters, and ultra-high-linearity synaptic devices. This work underscores the promise of liquid-enabled contact strategies for advancing low-power, flexible electronics and soft robotic systems.
Multiproposal Markov chain Monte Carlo (MCMC) algorithms choose from multiple proposals to generate their next chain step in order to sample from challenging target distributions more efficiently. However, on classical machines, these algorithms require 𝒪 ( P ) target evaluations for each Markov chain step when choosing from P proposals. Recent work demonstrates the possibility of quadratic quantum speedups for one such multiproposal MCMC algorithm. After generating P proposals, this quantum parallel MCMC (QPMCMC) algorithm requires only 𝒪 ( P ) target evaluations at each step, outperforming its classical counterpart. However, generating P proposals using classical computers still requires 𝒪 ( P ) time complexity, resulting in the overall complexity of QPMCMC remaining 𝒪 ( P ) . Here, we present a new, faster quantum multiproposal MCMC strategy, QPMCMC2. With a specially designed Tjelmeland distribution that generates proposals close to the input state, QPMCMC2 requires only 𝒪 ( 1 ) target evaluations and 𝒪 ( log P ) qubits when computing over a large number of proposals P . Unlike its slower predecessor, the QPMCMC2 Markov kernel (1) maintains detailed balance exactly and (2) is fully explicit for a large class of graphical models. We demonstrate this flexibility by applying QPMCMC2 to novel Ising-type models built on bacterial evolutionary networks and obtain significant speedups for Bayesian ancestral trait reconstruction for 248 observed salmonella bacteria.
This article comprehensively reviews the technological advancements, emerging materials, processing techniques adopted (atomic layer deposition, atomic layer etching, and neutral beam etching), geometric influences, and fabrication challenges in the development of advanced semiconductor devices. These technologies are recognized for their precision at the atomic scale and are crucial in fabricating next-generation silicon photonics optoelectronic devices. They also play an important role in the development of RF/power third-generation compound semiconductors and advanced semiconductor devices. Atomic layer deposition (ALD) offers superior control over thin film growth, ensuring uniformity and material conformity. Atomic layer etching (ALE) enables precise layer-by-layer material removal, making it ideal for high-aspect-ratio structures. Neutral beam etching (NBE) minimizes surface damage, a key factor in maintaining device reliability, particularly for GaN-based semiconductors. This article also assesses the role of these technologies in enhancing semiconductor device performance, with a focus on overcoming the limitations of traditional methods. The combined application of ALD, ALE, and NBE technologies is driving innovations in advanced semiconductor fabrication, making these processes indispensable for advancements in areas such as micro-LEDs, optical communication, and high-frequency, high-power electronic devices.
The silicon-based field-effect transistor (FET) is approaching the physical limits for the prominent short-channel effects and the sequent leakage currents under the conventional paradigm. Here, we propose a momentum-dependent field-effect transistor (MD-FET) to address this issue, in which a monolayer 2D semiconductor is sandwiched by two cross 1D carbon nanotube electrodes. The MD-FET enables a perfect off state, as the elastic tunneling is forbidden by the momentum mismatch between the cross 1D contacts. It can also access a substantial on state, because the momentum mismatch can be compensated by the electron-phonon scattering in a 2D channel. The MD-FET with sub-1-nm channel thus exhibits high on/off ratios of ~107, which breaks through the theoretical limit on the short-channel effect. The MD-FET opens up a previously unknown paradigm to further scale down transistors beyond silicon and inspires a promising solution for the post-Moore era.
The rapid evolution of quantum devices fuels concerted efforts to experimentally establish quantum advantage over classical computing. Many demonstrations of quantum advantage, however, rely on computational assumptions and face verification challenges. Furthermore, steady advances in classical algorithms and machine learning make the issue of provable, practically demonstrable quantum advantage a moving target. In this work, we unconditionally demonstrate that parallel quantum computation can exhibit greater computational power than previously recognized. We prove that polynomial-size biased threshold circuits of constant depth-which model neural networks with tunable expressivity-fail to solve certain problems solvable by small constant-depth quantum circuits with local gates, for values of the bias that allow quantifiably large computational power. Additionally, we identify a family of problems that are solvable in constant depth by a universal quantum computer over prime-dimensional qudits with bounded connectivity, but remain hard for polynomial-size biased threshold circuits. We thereby bridge the foundational theory of non-local games in higher dimensions with computational advantage on emerging devices operating on a wide range of physical platforms. Finally, we show that these quantum advantages are robust to noise across all prime qudit dimensions with all-to-all connectivity, enhancing their practical appeal.