Search Results (867)

Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts. Full article

Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and technological advancements accelerate, classical machine learning algorithms increasingly face difficulties in solving complex real-world problems. The integration of classical machine learning with quantum information processing has led to the emergence of quantum machine learning, a promising interdisciplinary field. This work provides the reader with a bottom-up view of quantum circuits starting from quantum data representation, quantum gates, the fundamental quantum algorithms, and more complex quantum processes. Thoroughly studying the mathematics behind them is a powerful tool to guide scientists entering this domain and exploring their connection to quantum machine learning. Quantum algorithms such as Shor’s algorithm, Grover’s algorithm, and the Harrow–Hassidim–Lloyd (HHL) algorithm are discussed in detail. Furthermore, real-world implementations of quantum machine learning and quantum deep learning are presented in fields such as healthcare, bioinformatics and finance. These implementations aim to enhance time efficiency and reduce algorithmic complexity through the development of more effective quantum algorithms. Therefore, a comprehensive understanding of the fundamentals of these algorithms is crucial. Full article

This follow-up scientific case study builds on prior research to explore the computational challenges of applying quantum algorithms to financial asset management, focusing specifically on solving the graph-cut problem for investment recommendation. Unlike our prior study, which focused on idealized QAOA performance, this work introduces a graph compression pipeline that enables QAOA deployment under real quantum hardware constraints. This study investigates quantum-accelerated spectral graph compression for financial asset recommendations, addressing scalability and regulatory constraints in portfolio management. We propose a hybrid framework combining the Quantum Approximate Optimization Algorithm (QAOA) with spectral graph theory to solve the Max-Cut problem for investor clustering. Our methodology leverages quantum simulators (cuQuantum and Cirq-GPU) to evaluate performance against classical brute-force enumeration, with graph compression techniques enabling deployment on resource-constrained quantum hardware. The results underscore that efficient graph compression is crucial for successful implementation. The framework bridges theoretical quantum advantage with practical financial use cases, though hardware limitations (qubit counts, coherence times) necessitate hybrid quantum-classical implementations. These findings advance the deployment of quantum algorithms in mission-critical financial systems, particularly for high-dimensional investor profiling under regulatory constraints. Full article

The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling. Full article

The increasing integration of wind and photovoltaic energy into power systems brings about large fluctuations and significant challenges for power absorption. Wind–solar–hydro–storage multi-energy complementary systems, especially joint dispatching strategies, have attracted wide attention due to their ability to coordinate the advantages of different resources and enhance both flexibility and economic efficiency. This paper develops a capacity optimization model for a wind–solar–hydro–storage multi-energy complementary system. The objectives are to improve net system income, reduce wind and solar curtailment, and mitigate intraday fluctuations. We adopt the quantum particle swarm algorithm (QPSO) for outer-layer global optimization, combined with an inner-layer stepwise simulation to maximize life cycle benefits under multi-dimensional constraints. The simulation is based on the output and load data of typical wind, solar, water, and storage in Yunnan Province, and verifies the effectiveness of the proposed model. The results show that after the wind–solar–hydro–storage multi-energy complementary system is optimized, the utilization rate of new energy and the system economy are significantly improved, which has a wide range of engineering promotion value. The research results of this paper have important reference significance for the construction of new power systems and the engineering design of multi-energy complementary projects. Full article

Carbon nanotube field-effect transistors (CNTFETs) are becoming a strong competitor for the next generation of high-performance, energy-efficient integrated circuits due to their near-ballistic carrier transport characteristics and excellent suppression of short-channel effects. However, CNT FETs with large diameters and small band gaps exhibit obvious bipolarity, and gate-induced drain leakage (GIDL) contributes significantly to the off-state leakage current. Although the asymmetric gate strategy and feedback gate (FBG) structures proposed so far have shown the potential to suppress CNT FET leakage currents, the devices still lack scalability. Based on the analysis of the conduction mechanism of existing self-aligned gate structures, this study innovatively proposed a design strategy to extend the length of the source–drain epitaxial region (L_ext) under a vertically stacked architecture. While maintaining a high drive current, this structure effectively suppresses the quantum tunneling effect on the drain side, thereby reducing the off-state leakage current (I_off = 10⁻¹⁰ A), and has good scaling characteristics and leakage current suppression characteristics between gate lengths of 200 nm and 25 nm. For the sidewall gate architecture, this work also uses single-walled carbon nanotubes (SWCNTs) as the channel material and uses metal source and drain electrodes with good work function matching to achieve low-resistance ohmic contact. This solution has significant advantages in structural adjustability and contact quality and can significantly reduce the off-state current (I_off = 10⁻¹⁴ A). At the same time, it can solve the problem of off-state current suppression failure when the gate length of the vertical stacking structure is 10 nm (the total channel length is 30 nm) and has good scalability. Full article

Amyloid-β (Aβ) aggregates are considered as the important factors of Alzheimer’s disease (AD). Multifunctional materials have shown significant effects in the diagnosis and treatment of AD by modulating the aggregation of Aβ and production of reactive oxygen species (ROS). Compared to traditional surgical treatment and radiotherapy, phototherapy has the advantages, including short response time, significant efficacy, and minimal side effects in disease diagnosis and treatment. Recent studies have shown that local thermal energy or singlet oxygen generated by irradiating certain organic molecules or nanomaterials with specific laser wavelengths can effectively degrade Aβ aggregates and depress the generation of ROS, promoting progress in AD diagnosis and therapy. Herein, we outline the development of photothermal therapy (PTT) and photodynamic therapy (PDT) strategies for the diagnosis and therapy of AD by modulating Aβ aggregation. The materials mainly include organic photothermal agents or photosensitizers, polymer materials, metal nanoparticles, quantum dots, carbon-based nanomaterials, etc. In addition, compared to traditional fluorescent dyes, aggregation-induced emission (AIE) molecules have the advantages of good stability, low background signals, and strong resistance to photobleaching for bioimaging. Some AIE-based materials exhibit excellent photothermal and photodynamic effects, showing broad application prospects in the diagnosis and therapy of AD. We further summarize the advances in the detection of Aβ aggregates and phototherapy of AD using AIE-based materials. Full article

Organic polymer introduction effectively enhances the toughness, bond strength, and durability of ordinary cement-based materials, and is often used for concrete repair and reinforcement. However, the entrained air effect simultaneously induced by polymer and the inhibitory action on cement hydration kinetics often lead to degradation in mechanical performances of polymer-modified cement-based composite (PMC). Nanomaterials provide unique advantages in enhancing the properties of PMC due to their characteristic ultrahigh specific surface area, quantum effects, and interface modulation capabilities. This review systematically examines recent advances in nano-reinforced PMC (NPMC), elucidating their synergistic optimization mechanisms. The synergistic effects of nanomaterials—nano-nucleation, pore-filling, and templating mechanisms—refine the microstructure, significantly enhancing the mechanical strength, impermeability, and erosion resistance of NPMC. Furthermore, nanomaterials establish interpenetrating network structures (A composite structure composed of polymer networks and other materials interwoven with each other) with polymer cured film (The film formed after the polymer loses water), enhancing load-transfer efficiency through physical and chemical action while optimizing dispersion and compatibility of nanomaterials and polymers. By systematically analyzing the synergy among nanomaterials, polymer, and cement matrix, this work provides valuable insights for advancing high-performance repair materials. Full article

Quantum private comparison (QPC) is a quantum cryptographic protocol designed to enable two mutually distrustful parties to securely compare sensitive data without disclosing their private information to each other or any external entities. This study proposes a novel QPC protocol that leverages Bell states to ensure data privacy, utilizing the fundamental principles of quantum mechanics. Within this framework, two participants, each possessing a secret integer, encode the binary representation of their values using Pauli-X and Pauli-Z operators applied to quantum states transmitted from a semi-honest third party (TP). The TP, which is bound to protocol compliance and prohibited from colluding with either participant, measures the received sequences to determine the comparison result without accessing the participants’ original inputs. Theoretical analyses and simulations validate the protocol’s strong security, high efficiency, and practical feasibility in quantum computing environments. An advantage of the proposed protocol lies in its optimized utilization of Bell states, which enhances qubit efficiency and experimental practicality. Moreover, the proposed protocol outperforms several existing Bell-state-based QPC schemes in terms of efficiency. Full article

Interest in germanium electronic devices is experiencing a comeback thanks to their suitability for a wide range of new applications, like CMOS transistors, quantum technology or infrared photonics. Among these applications, Ge-based thermophotovoltaic converters could become the backbone of thermo-electrical batteries. However, these devices are still far from the efficiency threshold needed for industrial deployment, with surface recombination as the main limiting factor for the material. In this work, we discuss the main passivation techniques developed for germanium photovoltaic and thermophotovoltaic devices, summarizing their main advantages and disadvantages. The analysis reveals that surface recombination velocities as low as 2.7 cm/s and 1.3 cm/s have already been reported for p-type and n-type germanium, respectively, although improving surface recombination velocities below 100 cm/s would result in marginal efficiency gains. Therefore, the main challenge for the material is not reducing this parameter further but developing robust and reliable processes for integrating the current techniques into functional devices. Full article

In the era of rapid industrial automation advancements, the complexity of intelligent manufacturing equipment has been steadily escalated. Stringent demands for high-efficiency and high-precision diagnosis are increasingly being unmet by conventional fault diagnosis methods. To address these challenges, a novel fault diagnosis approach grounded in quantum Q-learning is presented in this paper. The distinct advantages of quantum computing are innovatively integrated with the decision-making framework of Q-learning through this method. By harnessing the multi-information-carrying capacities of qubits, vast amounts of multi-source heterogeneous data generated during equipment operation can be efficiently processed. Latent fault features are thereby rapidly uncovered, significantly reducing the time required for fault-feature extraction. Furthermore, optimal decisions can be dynamically formulated by Q-learning within evolving production environments, leveraging precise analysis outcomes from quantum computing. Real-time equipment status is continuously monitored to accurately identify fault types, pinpoint locations, and promptly generate targeted maintenance strategies. Fault-diagnosis tests conducted on typical industrial intelligent manufacturing equipment demonstrate that the quantum Q-learning method outperforms traditional approaches in terms of diagnosis accuracy, efficiency, and adaptability to complex fault patterns. This breakthrough opens up new frontiers for fault diagnosis in intelligent manufacturing systems. Full article

An altermagnet, characterized by its distinctive magnetic properties, may hold potential applications in diverse fields such as magnetic materials, spintronics, data storage, and quantum computing. As a prototypical altermagnet, RuO₂ exhibits spin polarization and demonstrates the advantageous characteristics of high electrical conductivity and low thermal conductivity. These exceptional properties endow it with considerable promise in the emerging field of thermal spintronics. We studied the electronic structure and thermoelectric properties of RuO₂; the constructed RuO₂/TiO₂/RuO₂ all-antiferromagnetic tunnel junction (AFMTJ) exhibited thermally induced magnetoresistance (TIMR), reaching a maximum TIMR of 1756% at a temperature gradient of 5 K. Compared with prior studies on RuO₂-based antiferromagnetic tunnel junctions, the novelty of this work lies in the thermally induced magnetoresistance based on its superior thermoelectric properties. In parallel structures, the spin-down current dominates the transmission spectrum, whereas in antiparallel structures, the spin-up current governs the transmission spectrum, underscoring the spin-polarized thermal transport. In addition, thermoelectric efficiency emphasizes the potential of RuO₂ to link antiferromagnetic robustness with ferromagnetic spin functionality. These findings promote the development of efficient spintronic devices and spin-based storage technology for waste heat recovery and emphasize the role of spin splitting in zero-magnetization systems. Full article

The increasing deployment of photovoltaic and energy storage systems (ESSs) in modern power grids has highlighted the critical challenge of component degradation, which significantly impacts system efficiency, operational costs, and long-term reliability. Conventional energy dispatch and optimization approaches fail to adequately mitigate the progressive efficiency loss in PV modules and battery storage, leading to suboptimal performance and reduced system longevity. To address these challenges, this paper proposes a quantum-enhanced degradation pathway optimization framework that dynamically adjusts operational strategies to extend the lifespan of PV storage systems while maintaining high efficiency. By leveraging quantum-assisted Monte Carlo simulations and hybrid quantum–classical optimization, the proposed model evaluates degradation pathways in real time and proactively optimizes energy dispatch to minimize efficiency losses due to aging effects. The framework integrates a quantum-inspired predictive maintenance algorithm, which utilizes probabilistic modeling to forecast degradation states and dynamically adjust charge–discharge cycles in storage systems. Unlike conventional optimization methods, which struggle with the complexity and stochastic nature of degradation mechanisms, the proposed approach capitalizes on quantum parallelism to assess multiple degradation scenarios simultaneously, significantly enhancing computational efficiency. A three-layer hierarchical optimization structure is introduced, ensuring real-time degradation risk assessment, periodic dispatch optimization, and long-term predictive adjustments based on PV and battery aging trends. The framework is tested on a 5 MW PV array coupled with a 2.5 MWh lithium-ion battery system, with real-world degradation models applied to reflect light-induced PV degradation (0.7% annual efficiency loss) and battery state-of-health deterioration (1.2% per 100 cycles). A hybrid quantum–classical computing environment, utilizing D-Wave’s Advantage quantum annealer alongside a classical reinforcement learning-based optimization engine, enables large-scale scenario evaluation and real-time operational adjustments. The simulation results demonstrate that the quantum-enhanced degradation optimization framework significantly reduces efficiency losses, extending the PV module’s lifespan by approximately 2.5 years and reducing battery-degradation-induced wear by 25% compared to conventional methods. The quantum-assisted predictive maintenance model ensures optimal dispatch strategies that balance energy demand with system longevity, preventing excessive degradation while maintaining grid reliability. The findings establish a novel paradigm in degradation-aware energy optimization, showcasing the potential of quantum computing in enhancing the sustainability and resilience of PV storage systems. This research paves the way for the broader integration of quantum-based decision-making in renewable energy infrastructure, enabling scalable, high-performance optimization for future energy systems. Full article

Quantum neural networks (QNNs) represent an emerging technology that uses a quantum computer for neural network computations. The QNNs have demonstrated potential advantages over classical neural networks in certain tasks. As a core component of a QNN, the parameterized quantum circuit (PQC) plays a crucial role in determining the QNN’s overall performance. However, quantum circuit architectures designed manually based on experience or using specific hardware structures can suffer from inefficiency due to the introduction of redundant quantum gates, which amplifies the impact of noise on system performance. Recent studies have suggested that the advantages of quantum evolutionary algorithms (QEAs) in terms of precision and convergence speed can provide an effective solution to quantum circuit architecture-related problems. Currently, most QEAs adopt a fixed rotation mode in the evolution process, and a lack of an adaptive updating mode can cause the QEAs to fall into a local optimum and make it difficult for them to converge. To address these problems, this study proposes an adaptive quantum evolution algorithm (AQEA). First, an adaptive mechanism is introduced to the evolution process, and the strategy of combining two dynamic rotation angles is adopted. Second, to prevent the fluctuations of the population’s offspring, the elite retention of the parents is used to ensure the inheritance of good genes. Finally, when the population falls into a local optimum, a quantum catastrophe mechanism is employed to break the current population state. The experimental results show that compared with the QNN structure based on manual design and QEA search, the proposed AQEA can reduce the number of network parameters by up to 20% and increase the accuracy by 7.21%. Moreover, in noisy environments, the AQEA-optimized circuit outperforms traditional circuits in maintaining high fidelity, and its excellent noise resistance provides strong support for the reliability of quantum computing. Full article

Electromagnetic absorbers combining ultrathin profiles with robust absorptivity across wide incidence angles are essential for applications such as stealth applications, wireless communications, and quantum computing. Traditional designs, including Salisbury screens, typically require thicknesses of at least a quarter-wavelength ($\lambda /4$), restricting their use in compact systems. While metamaterial absorbers (MMAs) offer reduced thicknesses, their absorptivity generally decreases under oblique incidence conditions. Here, we introduce an adjoint optimization-based inverse design method that merges the ultrathin advantage of MMAs with the angle-insensitive characteristics of Salisbury screens. By leveraging the computational efficiency of the adjoint method, we systematically optimize absorber structures as thin as $\lambda /20$. The optimized structures achieve absorption exceeding 90% at the target frequency (7.5 GHz) and demonstrate robust performance under oblique incidence, maintaining over 90% absorption up to 50°, approximately 80% at 60°, and around 70% at 70°. Comparative analysis against particle swarm optimization further highlights the superior efficiency of the adjoint method, reducing the computational effort by approximately 98%. This inverse design framework thus provides substantial improvements in both the performance and computational cost, offering a promising solution for advanced electromagnetic absorber design. Full article