Search Results (29)

Background: Quantum Machine Learning (QML) has attracted significant attention in recent years. With quantum computing achievements in computationally costly domains, discovering its potential in improving the performance and efficiency of deep learning models in medical imaging has become a promising field of research. Methods: We investigate QML in healthcare by developing a novel quantum-enhanced U-Net (QU-Net). We experiment with six configurations of parameterized quantum circuits, varying the encoding technique (amplitude vs. angle), depth and entanglement. Using the ISIC-2017 skin cancer dataset, we compare QU-Net with classical U-Net on self-supervised image reconstruction and binary classification of benign and malignant skin cancer, where we combine bottleneck embeddings with patient metadata. Results: Our findings show that amplitude encoding stabilizes training, whereas angle encoding introduces fluctuations. The best performance is obtained with amplitude encoding and one layer. For reconstruction, QU-Net with entanglement converges faster (25 epochs vs. 44) with a lower Mean Squared Error per image (0.00015 vs. 0.00017) on unseen data. For classification, QU-Net with no entanglement embeddings reaches 79.03% F1-score compared with 74.14% for U-Net, despite compressing images to a smaller latent space (7 vs. 128). Conclusions: These results demonstrate that the quantum layer enhances U-Net’s expressive power with efficient data embedding. Full article

Current private comparison schemes primarily focus on comparing single secret integers using quantum technologies, while the area of private array content comparison remains relatively unexplored. To bridge this gap, we introduce a quantum private array content comparison (QPACC) scheme based on multi-qubit swap test. This scheme integrates rotation operation, quantum homomorphic encryption (QHE), and multi-qubit swap test to facilitate the equality comparison of array contents while ensuring their confidentiality. In our approach, participants encode their array elements into the phases of quantum states using rotation operations, which are then encrypted via QHE. These encrypted quantum states are sent to a semi-honest third party (TP) who decrypts the encoded quantum states and computes the modulus squared sum of the inner products of these decoded quantum states using the multi-qubit swap test, thereby determining the equality relationship of the array contents. To verify the feasibility of the proposed scheme, we conduct a case simulation using IBM Qiskit. Security analysis indicates that the proposed scheme is resistant to quantum attacks (including intercept-resend, entangle-measure, and quantum Trojan horse attacks) from outsider eavesdroppers and attempts by curious participants. Full article

To address the problem of the bottom velocity being directly affected by the time-varying ocean currents when DVL operates in the water observation mode, it cannot be directly used for combined SINS/DVL navigation. Existing methods generally approximate small-scale, short-term currents as constant; however, this assumption is inconsistent with reality over longer durations. When the conventional Kalman filter (KF) algorithm incorporates currents into the state vector, their velocities become entangled with the SINS errors, limiting estimation accuracy. This paper proposes an augmented observation algorithm (AOA) that achieves error decoupling by enhancing DVL observation and deriving the observable current velocity equation without needing external observation information. This approach effectively estimates time-varying currents. The results from simulations and shipboard tests show that, compared to the reference algorithm (Augmented Observation Quantity Filtering algorithm (AOQ)), the proposed AOA significantly decreases the root mean square error (RMSE) of time-varying current velocity estimation by more than 67%. Additionally, the RMSE of the positioning accuracy of the combined SINS/DVL navigation is improved by over 68%. Full article

Ensuring the safety, reliability, and longevity of Lithium-ion (Li-ion) batteries is crucial for sustainable integration of Electric Vehicles (EVs) within Intelligent Transportation Systems (ITSs). However, thermal stress and degradation-induced anomalies can cause sudden performance failures, posing critical operational and safety risks. Capturing complex, non-linear, and high-dimensional patterns remains challenging for traditional Machine Learning (ML) models. We propose a hybrid anomaly detection method that incorporates a Variational Quantum Neural Network (VQNN), which uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to learn complex non-linear patterns. The VQNN is integrated with Isolation Forest (IF) and a Median Absolute Deviation (MAD)-based spike characterisation method to form a Quantum Anomaly Detector (QAD). This method distinguishes between normal and anomalous spikes in battery behaviour. Using an Arrhenius-based model, we simulate how the State of Health (SoH) and voltage of a Li-ion battery reduce as temperatures increase. We perform experiments on NASA battery datasets and detect abnormal spikes in 14 out of 168 cycles, corresponding to $8.3$% of the cycles. The QAD achieves the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of $0.9820$, outperforming the baseline IF model by $7.78\%$. We use ML to predict the SoH and voltage changes when the temperature varies. Gradient Boosting (GB) achieves a voltage Mean Squared Error (MSE) of $0.001425$, while Support Vector Regression (SVR) achieves the highest $R^2$ score of $0.9343$. These results demonstrate that Quantum Machine Learning (QML) can be applied for anomaly detection in Battery Management Systems (BMSs) within intelligent transportation ecosystems and could enable EVs to autonomously adapt their routing and schedule preventative maintenance. With these capabilities, safety will be improved, downtime minimised, and public confidence in sustainable transport technologies increased. Full article

This paper introduces the concept of degenerative affordances to explain how social media can unintentionally destabilise family-run influencer businesses. While affordance theory typically highlights the enabling features of technology, the researchers shift the focus to its unintended, risk-laden consequences, particularly within family enterprises where professional and personal identities are deeply entangled. Drawing on platform capitalism, family business research, and intersectional feminist critiques, the researchers develop a theoretical model to examine how social media affordances contribute to role confusion, privacy breaches, and trust erosion. Using a mixed-methods design, the researchers combine narrative interviews (n = 20) with partial least squares structural equation modelling (PLS-SEM) on survey data (n = 320) from family-based influencers. This study’s findings reveal a high explanatory power (R² = 0.934) for how digital platforms mediate entrepreneurial legitimacy through interpersonal trust and role dynamics. Notably, trust emerges as a key mediating mechanism linking social media engagement to perceptions of business legitimacy. This paper advances three core contributions: (1) introducing degenerative affordance as a novel extension of affordance theory; (2) unpacking how digitally mediated role confusion and privacy breaches function as internal threats to legitimacy in family businesses; and (3) problematising the epistemic assumptions embedded in entrepreneurial legitimacy itself. This study’s results call for a rethinking of how digital platforms, family roles, and entrepreneurial identities co-constitute each other under the pressures of visibility, intimacy, and algorithmic governance. The paper concludes with implications for influencer labour regulation, platform accountability, and the ethics of digital family entrepreneurship. Full article

We introduce a quantum integrated-information measure $\Phi$ for multipartite states within the Relational Quantum Dynamics (RQD) framework. $\Phi \left(\rho \right)$ is defined as the minimum quantum Jensen–Shannon distance between an n-partite density operator $\rho$ and any product state over a bipartition of its subsystems. We prove that its square root induces a genuine metric on state space and that $\Phi$ is monotonic under all completely positive trace-preserving maps. Restricting the search to bipartitions yields a unique optimal split and a unique closest product state. From this geometric picture, we derive a canonical entanglement witness directly tied to $\Phi$ and construct an integration dendrogram that reveals the full hierarchical correlation structure of $\rho$. We further show that there always exists an “optimal observer”—a channel or basis—that preserves $\Phi$ better than any alternative. Finally, we propose a quantum Markov blanket theorem: the boundary of the optimal bipartition isolates subsystems most effectively. Our framework unites categorical enrichment, convex-geometric methods, and operational tools, forging a concrete bridge between integrated information theory and quantum information science. Full article

Natural restoration of vegetation and plantation are effective land use measures to promote soil organic carbon (SOC) sequestration. How soil physicochemical properties, microorganisms, Glomalin-related soil proteins (GRSPs), and aggregates interact to regulate SOC accumulation and sequestration remains unclear. This study examined five land uses in the karst region of Southwest China: corn field (CF), corn intercropped with cabbage fields (CICF), orchard (OR), plantation (PL), and natural restoration of vegetation (NRV). The results revealed that SOC, total nitrogen (TN), total phosphorus (TP), total GRSP (T-GRSP), and easily extractable GRSP (EE-GRSP) contents were significantly higher under NRV and PL than in the CF, CICF, and OR, with increases ranging from 10.69% to 266.72%. Land use significantly influenced bacterial α-diversity, though fungal α-diversity remained unaffected. The stability of soil aggregates among the five land uses followed the order: PL > NRV > CF > OR > CICF. Partial least-squares path modeling (PLS-PM) identified land use as the most critical factor influencing SOC. SOC accumulation and stability were enhanced through improved soil properties, increased microbial diversity, and greater community abundance, promoting GRSP secretion and strengthening soil aggregate stability. In particular, soil microorganisms adhere to the aggregates of soil particles through the entanglement of fine roots and microbial hyphae and their secretions (GRSPs, etc.) to maintain the stability of the aggregates, thus protecting SOC from decomposition. Natural restoration of vegetation and plantation proved more effective for soil carbon sequestration in the karst region of Southwest China compared to sloping cropland and orchards. Full article

This manuscript introduces a comprehensive framework for augmenting classical statistical methodologies through the targeted integration of core quantum mechanical principles—specifically superposition, entanglement, measurement, wavefunctions, and density matrices. By concentrating on these foundational concepts instead of the whole expanse of quantum theory, we propose “quantum-inspired” models that address persistent shortcomings in conventional statistical approaches. In particular, five pivotal distributions (normal, binomial, Poisson, Student’s t, and chi-square) are reformulated to incorporate interference terms, phase factors, and operator-based transformations, thereby facilitating the representation of multimodal data, phase-sensitive dependencies, and correlated event patterns—characteristics that are frequently underrepresented in purely real-valued, classical frameworks. Furthermore, ten quantum-inspired statistical principles are delineated to guide practitioners in systematically adapting quantum mechanics for traditional inferential tasks. These principles are illustrated through domain-specific applications in finance, cryptography (distinct from direct quantum cryptography applications), healthcare, and climate modeling, demonstrating how amplitude-based confidence measures, density matrices, and measurement analogies can enrich standard statistical models by capturing more nuanced correlation structures and enhancing predictive performance. By unifying quantum constructs with established statistical theory, this work underscores the potential for interdisciplinary collaboration and paves the way for advanced data analysis tools capable of addressing high-dimensional, complex, and dynamically evolving datasets. Complete R code ensures reproducibility and further exploration. Full article

Entanglement plays a pivotal role in numerous quantum applications, and as technology progresses, entanglement systems continue to expand. However, quantifying entanglement is a complex problem, particularly for multipartite quantum states. The currently available entanglement measures suffer from high computational complexity, and for unknown multipartite entangled states, complete information about the quantum state is often necessary, further complicating calculations. In this paper, we train neural networks to quantify unknown multipartite entanglement using input features based on squared entanglement (SE) and outcome statistics data produced by locally measuring target quantum states. By leveraging machine learning techniques to handle non-linear relations between outcome statistics and entanglement measurement SE, we achieve high-precision quantification of unknown multipartite entanglement states with a linear number of measurements, avoiding the need for global measurements and quantum state tomography. The proposed method exhibits robustness against noise and extends its applicability to pure and mixed states, effectively scaling to large-scale multipartite entanglement systems. The results of the experiment show that the predicted entanglement measures are very close to the actual values, which confirms the effectiveness of the proposed method. Full article

In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model that leverages the intricate processing capabilities of quantum computing combined with the robust pattern recognition prowess of deep learning algorithms such as Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short-Term Memory (Bi-LSTM). Bi-LSTM networks are used for temporal feature extraction and quantum circuits for quantum feature processing. Quantum circuits leverage quantum superposition and entanglement to enhance data representation by capturing intricate feature interactions. These enriched quantum features are combined with the temporal features extracted by Bi-LSTM and fed into an XGBoost regressor. By synthesizing quantum feature processing and classical machine learning techniques, our model aims to improve prediction accuracy significantly. Based on measurements of mean square error (MSE), the coefficient of determination ($R^2$), and mean average error (MAE), the results are 1.191621 × 10⁻⁵, 0.999929482, and 0.001392724, respectively. This value is so close to perfect that it helps make essential decisions in global agricultural planning and management. Full article

To estimate the degree of quantum entanglement of random pure states, it is crucial to understand the statistical behavior of entanglement indicators such as the von Neumann entropy, quantum purity, and entanglement capacity. These entanglement metrics are functions of the spectrum of density matrices, and their statistical behavior over different generic state ensembles have been intensively studied in the literature. As an alternative metric, in this work, we study the sum of the square root spectrum of density matrices, which is relevant to negativity and fidelity in quantum information processing. In particular, we derive the finite-size mean and variance formulas of the sum of the square root spectrum over the Bures–Hall ensemble, extending known results obtained recently over the Hilbert–Schmidt ensemble. Full article

The Lieb-Robinson correlation function is one way to capture the propagation of quantum entanglement and correlations in many-body systems. We consider arrays of qubits described by the tranverse-field Ising model and examine correlations as the expanding front of entanglement first reaches a particular qubit. Rather than a new bound for the correlation function, we calculate its value, both numerically and analytically. A general analytical result is obtained that enables us to analyze very large arrays of qubits. The velocity of the entanglement front saturates to a constant value, for which an analytic expression is derived. At the leading edge of entanglement, the correlation function is well-described by an exponential reduced by the square root of the distance. This analysis is extended to arbitrary arrays with general coupling and topologies. For regular two and three dimensional qubit arrays with near-neighbor coupling we find the saturation values for the direction-dependent Lieb-Robinson velocity. The symmetry of the underlying 2D or 3D lattice is evident in the shape of surfaces of constant entanglement, even as the correlations front expands over hundreds of qubits. Full article

Retrogradation of cooked rice happens in two ways: one is by the formation of ordered structures, and the other is through intra- and inter-chain entanglement and inter-chain overlap, which in turn are affected by the amylose chain-length distribution. Both entanglement and overlap could affect rice texture. Here, four amylose samples were isolated from starch by precipitation from a dimethyl sulfoxide solution with butan-1-ol and isoamyl alcohol. Following enzymatic debranching, they were then characterized using size-exclusion chromatography. Amylose solutions (10%, m/v) were made by dissolving amylose in 90% (v/v) DMSO. Amylose gels (10%, w/v) were made by dissolving amylose powders into hot water, followed by cooling. The rigidity of the amylose gels and the structural order were measured using a texture analyzer and X-ray diffractometer, respectively. In the amylose solution, for a given mass of polymer in a fixed amount of solvent, the total occupied volume was reduced when the polymer molecular weight was smaller, resulting in less inter-chain overlap and a lower viscosity of the amylose solution. The overall mobility and diffusion of the molecules were inversely related to the square of the molecular weight until the gelation concentration. Thus, amylose gels in which amylose had a lower molecular weight had a greater chance to permeate into other molecules, which counterintuitively led to more inter-chain entanglement and more rigid amylose gels during retrogradation. This information could help rice breeders improve rice quality by using the molecular structure of starch as a guide. Full article

Adding drag reduction agent (DRA) to rocket kerosene is an effective way to reduce the pipeline resistance of rocket kerosene transportation systems. However, so far, there have been few research reports on the effect of DRA on the rheological properties of rocket kerosene solution, especially from a microscopic perspective. In this study, coarse-grained molecular dynamics simulations were conducted to investigate the rheological properties of rocket kerosene solutions with DRAs of different chain lengths and concentrations. The results showed that the viscosity of DRA—kerosene solution is generally higher than that of pure kerosene at a low shear rate, while with an increase in shear rate, the viscosity of DRA—kerosene solution decreases rapidly and finally tends to become similar to that of pure kerosene. The shear viscosity of DRA—kerosene solution increases with an increase in chain length and concentration of polymers. Through observing the morphologic change of DRA molecules and analyzing the radius of gyration and the mean-squared end-to-end distance of polymers, it was confirmed that the rheological properties of DRA—kerosene solutions are strongly related to the degree of entanglement of polymer chains. The simulation results provide microscopic insights into the rheological behavior of DRA—kerosene solutions and clarify the intrinsic relation between the morphologic change of polymer molecules and the rheological properties of DRA—kerosene solutions. Full article

This paper studies the effect of quantum computers on Bitcoin mining. The shift in computational paradigm towards quantum computation allows the entire search space of the golden nonce to be queried at once by exploiting quantum superpositions and entanglement. Using Grover’s algorithm, a solution can be extracted in time $O\left(\sqrt{2^{256}/t}\right)$, where t is the target value for the nonce. This is better using a square root over the classical search algorithm that requires $O(2^{256}/t)$ tries. If sufficiently large quantum computers are available for the public, mining activity in the classical sense becomes obsolete, as quantum computers always win. Without considering quantum noise, the size of the quantum computer needs to be $\approx$${10}^4$ qubits. Full article