Search Results (33)

Forecasting national energy demand is challenging under climate variability and macroeconomic uncertainty. We assess whether hybrid Prophet–(Q)LSTM models that integrate climate and economic predictors improve long-horizon forecasts for The Netherlands. This study covers 2010–2024 and uses data from ENTSO-E (hourly load), KNMI and Copernicus/ERA5 (weather and climate indices), Statistics Netherlands (CBS), and the World Bank (macroeconomic and commodity series). We evaluate Prophet–LSTM and Prophet–QLSTM, each with and without stacking via XGBoost, under rolling-origin cross-validation; feature choice is guided by Bayesian optimisation. Stacking provides the largest and most consistent accuracy gains across horizons. The quantum-inspired variant performs on par with the classical ensemble while using a smaller recurrent core, indicating value as a complementary learner. Substantively, short-run variation is dominated by weather and calendar effects, whereas selected commodity and activity indicators stabilise longer-range baselines; combining both domains improves robustness to regime shifts. In sustainability terms, improved long-horizon accuracy supports renewable integration, resource adequacy, and lower curtailment by strengthening seasonal planning and demand-response scheduling. The pipeline demonstrates the feasibility of integrating quantum-inspired components into national planning workflows, using The Netherlands as a case study, while acknowledging simulator constraints and compute costs. Full article

The stacking arrangement of layered covalent organic frameworks (LCOFs) critically influences their structure and function. We present a fully ab initio-based workflow to characterize stacking disorder in COF-1, combining simulated powder X-ray diffraction (PXRD) with stacking energy landscape analysis. By comparing PXRD patterns of idealized eclipsed, inclined, serrated, and staggered stacking with experiment, we rule out periodic high-symmetry motifs. A comprehensive “PXRD reference guide” links specific diffraction features to slip directions and magnitudes, providing a blueprint for the interpretation of experimental data of slipped structures. Quantum-mechanical potential energy surfaces reveal multiple symmetry-equivalent minima separated by small barriers. This makes diverse slip configurations thermally accessible and large-scale stacking disorder inevitable. Nevertheless, as staggered configurations are found to be energetically disfavored, open pore channels prevail despite the disorder. From the energy landscapes, we construct static disordered models using Boltzmann-weighted probabilities, where also the question is addressed, which energies should be used for actually calculating the Boltzmann weights. Simulated PXRD patterns from these models excellently reproduce experimental peak positions, shapes, and stacking distances, suggesting the dominance of disordered stacking not only in COF-1. Full article

Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions. Full article

Solvents represent the quiet majority in biomolecular systems, yet modeling their influence with both speed and ri:gor remains a central challenge. This study maps the state of the art in implicit solvent theory and practice, spanning classical continuum electrostatics (PB/GB; DelPhi, APBS), modern nonpolar and cavity/dispersion treatments, and quantum–continuum models (PCM, COSMO/COSMO-RS, SMx/SMD). We highlight where these methods excel and where they falter, namely, around ion specificity, heterogeneous interfaces, entropic effects, and parameter sensitivity. We then spotlight two fast-moving frontiers that raise both accuracy and throughput: machine learning-augmented approaches that serve as PB-accurate surrogates, learn solvent-averaged potentials for MD, or supply residual corrections to GB/PB baselines, and quantum-centric workflows that couple continuum solvation methods, such as IEF-PCM, to sampling on real quantum hardware, pointing toward realistic solution-phase electronic structures at emerging scales. Applications across protein–ligand binding, nucleic acids, and intrinsically disordered proteins illustrate how implicit models enable rapid hypothesis testing, large design sweeps, and long-time sampling. Our perspective argues for hybridization as a best practice, meaning continuum cores refined by improved physics, such as multipolar water, ML correctors with uncertainty quantification and active learning, and quantum–continuum modules for chemically demanding steps. Full article

Cancer remains one of the most formidable challenges to human health; hence, developing effective treatments is critical for saving lives. An important strategy involves reactivating tumor suppressor genes, particularly p53, by targeting their negative regulator MDM2, which is essential in promoting cell cycle arrest and apoptosis. Leveraging a drug repurposing approach, we screened over 24,000 clinically tested molecules to identify new MDM2 inhibitors. A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. This approach significantly improved the predictive accuracy of our machine learning model for pIC₅₀ values, reducing RMSE by 21.6% and achieving state-of-the-art performance (R² = 0.87)—a substantial improvement over standard data preprocessing pipelines. The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. We identified two clinical CB1 antagonists, MePPEP and otenabant, and the statin drug atorvastatin as promising repurposing candidates based on their high predicted potency and binding affinity toward MDM2. Interactions with the related proteins MDM4 and BCL2 suggest these compounds may enhance p53 restoration through multi-target mechanisms. Quantum mechanical (ONIOM) optimizations and molecular dynamics simulations confirmed the stability and favorable interaction profiles of the selected protein–ligand complexes, resembling that of navtemadlin, a known MDM2 inhibitor. This multiscale, accuracy-boosted workflow introduces a novel data-curation strategy that substantially enhances AI model performance and enables efficient drug repurposing against challenging cancer targets. Full article

Glaucoma is a leading cause of irreversible blindness globally, with early diagnosis being crucial to preventing vision loss. Traditional diagnostic methods, including fundus photography, OCT imaging, and perimetry, often fall short in sensitivity and fail to integrate structural and functional data. This study proposes a novel multi-modal diagnostic framework that combines convolutional neural networks (CNNs), vision transformers (ViTs), and quantum-enhanced layers to improve glaucoma detection accuracy and efficiency. The framework integrates fundus images, OCT scans, and clinical biomarkers, leveraging their complementary strengths through a weighted fusion mechanism. Datasets, including the GRAPE and other public and clinical sources, were used, ensuring diverse demographic representation and supporting generalizability. The model was trained and validated using cross-entropy loss, L2 regularization, and adaptive learning strategies, achieving an accuracy of 96%, sensitivity of 94%, and an AUC of 0.97—outperforming CNN-only and ViT-only approaches. Additionally, the quantum-enhanced architecture reduced computational complexity from O(n²) to O (log n), enabling real-time deployment with a 40% reduction in FLOPs. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. This framework offers a scalable and clinically viable tool for early glaucoma detection, supporting personalized care and improving diagnostic workflows in ophthalmology. Full article

Heavy metal ion (HMI) contamination poses significant threats to public health and environmental safety, necessitating advanced detection technologies that are rapid, sensitive, and field-deployable. While conventional methods like atomic absorption spectroscopy (AAS) and inductively coupled plasma mass spectrometry (ICP-MS) remain prevalent, their limitations—including high costs, complex workflows, and lack of portability—underscore the urgent need for innovative alternatives. This review consolidates advancements in the last five years in microfluidic technologies for HMI detection, emphasizing their transformative potential through miniaturization, integration, and automation. We critically evaluate the synergy of microfluidics with cutting-edge materials (e.g., graphene and quantum dots) and detection mechanisms (electrochemical, optical, and colorimetric), enabling ultra-trace detection at parts-per-billion (ppb) levels. We highlight novel device architectures, such as polydimethylsiloxane (PDMS)-based labs-on-chip (LOCs), paper-based microfluidics, 3D-printed systems, and digital microfluidics (DMF), which offer unparalleled portability, cost-effectiveness, and multiplexing capabilities. Additionally, we address persistent challenges (e.g., selectivity and scalability) and propose future directions, including AI integration and sustainable fabrication. By bridging gaps between laboratory research and practical deployment, this review provides a roadmap for next-generation microfluidic solutions, positioning them as indispensable tools for global HMI monitoring. Full article

Modeling many-body quantum systems is widely regarded as one of the most promising applications for near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials science or strongly correlated systems. A promising avenue of research is to combine many-body physics with machine learning for the classification of distinct phases. We present a workflow that synergizes quantum computing, many-body theory, and quantum machine learning (QML) for studying strongly correlated systems. In particular, it can capture a putative quantum phase transition of the stereotypical strongly correlated system, the Hubbard model. Following the recent proposal of the hybrid quantum-classical algorithm for the two-site dynamical mean-field theory (DMFT), we present a modification that allows the self-consistent solution of the single bath site DMFT. The modified algorithm can be generalized for multiple bath sites. This approach is used to generate a database of zero-temperature wavefunctions of the Hubbard model within the DMFT approximation. We then use a QML algorithm to distinguish between the metallic phase and the Mott insulator phase to capture the metal-to-Mott insulator phase transition. We train a recently proposed quantum convolutional neural network (QCNN) and then utilize the QCNN as a quantum classifier to capture the phase transition region. This work provides a recipe for application to other phase transitions in strongly correlated systems and represents an exciting application of small-scale quantum devices realizable with near-term technology. Full article

Quantum core technologies (computing, sensing, imaging, communication) hold immense promise for revolutionizing cancer care. This paper explores their distinct capabilities in early-stage cancer diagnosis, improved clinical workflows, drug discovery, and personalized treatment. By overcoming challenges such as infrastructure and ethical considerations, these processes can unlock faster diagnoses, optimize therapies, and enhance patient outcomes. Full article

Purpose: The impact of hybrid quantum-classical neural network (NN) architectures with multiple backbones and quantum transformation as a data augmentation (DA) technique on image classification tasks was investigated using the CIFAR-10 and MedMNIST (DermaMNIST) datasets. These datasets were chosen for their relevance in general-purpose and medical-specific small-scale image classification, respectively. Methods: A series of quanvolutional transformations, utilizing random quantum circuits based on single-qubit rotation quantum gates (Y-axis, X-axis, and combined XY-axis transformations), were applied to create multiple quantum channels (QC) for input augmentation. By integrating these QCs with baseline convolutional NN architectures (LCNet050) and scalable hybrid NN architectures with multiple (n) backbones and separate QC (n) inputs (HNN-QCn), the scalability and performance enhancements offered by quantum-inspired data augmentation were evaluated. The proposed cross-validation workflow ensured reproducibility and systematic performance evaluation of hybrid models by mean and standard deviation values of metrics (such as accuracy and area under the curve (AUC) for the receiver operating characteristic). Results: The results demonstrated consistent performance improvements by AUC and accuracy in HNN-QCn models with the number n (where $n\in \{4,5,9,10,17,18\}$) of backbones and QC inputs across both datasets. The different improvement rates were observed for the smaller increase in AUC and the larger increase in accuracy as input complexity (number of backbones and QCs inputs) increases. It is assumed that the prediction probability distribution is becoming sharpened with the addition of backbones and QC inputs, leading to larger improvements in accuracy. At the same time, AUC reflects these changes more slowly unless the model’s ranking ability improves substantially. Conclusion: The findings highlight the scalability, robustness, and adaptability of HNN-QCn architectures, with superior performance by AUC (micro and macro) and accuracy across diverse datasets and potential for applications in high-stakes domains like medical imaging. These results underscore the utility of quantum transformations as a form of DA, paving the way for further exploration into the scalability and efficiency of hybrid architectures in complex datasets and real-world scenarios. Full article

Organic solar cells (OSCs) are a promising renewable energy technology due to their flexibility, lightweight nature, and cost-effectiveness. However, challenges such as inconsistent efficiency and low stability limit their widespread application. Addressing these issues requires extensive experimentation to optimize device performance, a process hindered by the complexity of OSC molecular structures and device architectures. Machine learning (ML) offers a solution by accelerating material discovery and optimizing performance through the analysis of large datasets and prediction of outcomes. This review explores the application of ML in advancing OSC technologies, focusing on predicting critical parameters such as power conversion efficiency (PCE), energy levels, and absorption spectra. It emphasizes the importance of supervised, unsupervised, and reinforcement learning techniques in analyzing molecular descriptors, processing data, and streamlining experimental workflows. Concludingly, integrating ML with quantum chemical simulations, alongside high-quality datasets and effective feature engineering, enables accurate predictions that expedite the discovery of efficient and stable OSC materials. By synthesizing advancements in ML-driven OSC research, the gap between theoretical potential and practical implementation can be bridged. ML can viably accelerate the transition of OSCs from laboratory research to commercial adoption, contributing to the global shift toward sustainable energy solutions. Full article

This study employs quantum chemical computational methods to predict the spectroscopic properties of fluorescent probes 2,6-bis(2-benzimidazolyl)pyridine (BBP) and (E)-3-(2-(1H-benzo[d]imidazol-2-yl)vinyl)-9-(2-(2-methoxyethoxy)ethyl)-9H-carbazole (BIMC). Using time-dependent density functional theory (TDDFT), we successfully predicted the fluorescence emission wavelengths of BBP under various protonation states, achieving an average deviation of 6.0% from experimental excitation energies. Molecular dynamics simulations elucidated the microscopic mechanism underlying BBP’s fluorescence quenching under acidic conditions. The spectroscopic predictions for BIMC were performed using the STEOM-DLPNO-CCSD method, yielding an average deviation of merely 0.57% from experimental values. Based on Einstein’s spontaneous emission formula and empirical internal conversion rate formulas, we calculated fluorescence quantum yields for spectral intensity calibration, enabling the accurate prediction of experimental spectra. To streamline the computational workflow, we developed and open-sourced the EasySpecCalc software v0.0.1 on GitHub, aiming to facilitate the design and development of fluorescent probes. Full article

In this paper, we introduce the workflow for converting qubit circuits represented by Open Quantum Assembly format (OpenQASM, also known as QASM) into the qudit form for execution on qudit hardware and provide a method for translating qudit experiment results back into qubit results. We present the comparison of several qudit transpilation regimes, which differ in decomposition of multicontrolled gates: qubit as ordinary qubit transpilation and execution, qutrit with $d=3$ levels and single qubit in qudit, and ququart with $d=4$ levels and 2 qubits per ququart. We provide several examples of transpiling circuits for trapped ion qudit processors, which demonstrate potential advantages of qudits. Full article

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal–chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug’s efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal–chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal–chelator complexes, with a direct impact for rational drug design of this important class of drugs. Full article

The project, developed by a team of Una Quantum, consists of the digital renovation of the Archaeological Civic Museum of the Municipality of Collelongo (AQ) through the realisation of its website. The action plan aimed to highlight the essential role of open software and open workflow in the field of cultural heritage research and management, focusing on the use of programmes for 3D reconstruction, the creation of virtual tours and GIS (geographical information system) and WebGIS software (LeafletJS V.1.7.1). The website is structured using a modern and dynamic user-friendly interface which is subdivided into three main sections: the virtual tour, the online catalogue and web maps. Full article