Next Generation Journal for The Young Researchers
New Generation Young Researchers Journal offers an innovative and inspiring publishing platform for young researchers. We aim to enable young scientists and researchers to share their work and make their voices heard in the academic world. Our journal presents the latest discoveries and developments in various fields of science and technology with a focus on interdisciplinary research.
Quantum Computing Approaches to Portfolio Optimization Under Risk and Market Uncertainty
Portfolio optimization under conditions of risk and market uncertainty remains a foundational challenge in financial economics and investment management. Classical optimization frameworks, including mean–variance models and stochastic programming approaches, often struggle with high-dimensional asset universes, nonlinear correlations, and dynamic market regimes. This paper examines quantum computing approaches to portfolio optimization under risk and market uncertainty, focusing on hybrid quantum–classical algorithms designed to enhance combinatorial optimization and probabilistic modeling. By integrating quantum annealing, variational quantum circuits, and quantum-enhanced sampling techniques with classical financial analytics, the study evaluates their potential to improve computational efficiency and scenario sensitivity. The analysis considers both theoretical advantages and practical limitations within the Noisy Intermediate-Scale Quantum (NISQ) era. The findings suggest that quantum-assisted optimization may complement traditional portfolio models by expanding feasible solution spaces and improving robustness under uncertainty, thereby contributing to more adaptive and resilient investment strategies.
quantum finance, portfolio optimization, risk management, quantum annealing, hybrid quantum–classical models, market uncertainty
Copyright (c) 2026 Next Generation Journal for The Young Researchers
Quantum-Driven Financial Decision-Making Models for Management Science and Strategic Planning
The increasing complexity of financial systems and strategic environments has intensified the need for advanced decision-making models capable of handling uncertainty, multidimensional risk, and dynamic optimization constraints. Classical management science tools—while robust—often face computational limitations when applied to high-dimensional portfolio optimization, capital allocation, and scenario-based strategic planning. This paper explores quantum-driven financial decision-making models for management science and strategic planning, emphasizing hybrid quantum–classical frameworks that integrate quantum optimization, probabilistic sampling, and advanced analytics into managerial decision systems. By examining applications in portfolio management, risk assessment, supply chain finance, and strategic resource allocation, the study evaluates the theoretical advantages and practical constraints of quantum-enhanced models in organizational contexts. The findings suggest that quantum-driven decision architectures may complement traditional financial models by expanding solution-space exploration and improving optimization efficiency, thereby enhancing strategic agility and long-term value creation.
quantum finance, management science, strategic planning, hybrid quantum–classical models, financial optimization, decision analytics
Scenario-Based Macroeconomic and Financial Forecasting Using Quantum-Enhanced Models
Macroeconomic and financial forecasting plays a central role in policy design, risk management, and capital allocation, yet traditional econometric and machine learning models often struggle with nonlinear dynamics, structural breaks, and high-dimensional uncertainty. This paper explores scenario-based macroeconomic and financial forecasting using quantum-enhanced models, examining how hybrid quantum–classical frameworks can improve predictive performance under complex and volatile conditions. By integrating quantum optimization, probabilistic sampling, and high-dimensional feature mapping with scenario analysis techniques, the study proposes an architecture for modeling systemic risk, regime shifts, and tail-event dynamics. The analysis evaluates theoretical advantages, computational constraints, and implementation challenges within the Noisy Intermediate-Scale Quantum (NISQ) era. The findings suggest that quantum-enhanced forecasting may complement classical approaches by expanding solution space exploration and enhancing scenario sensitivity, thereby contributing to more resilient macro-financial decision-making systems.
quantum finance, macroeconomic forecasting, scenario analysis, hybrid quantum–classical models, systemic risk, financial time series
Integrating ESG Metrics into Strategic Decision-Making: Challenges and Opportunities
The integration of Environmental, Social, and Governance (ESG) metrics into strategic decision-making has emerged as a defining feature of contemporary corporate governance and sustainable value creation. As stakeholders increasingly demand transparency, accountability, and long-term responsibility, firms face mounting pressure to embed ESG considerations into core business strategies rather than treating them as peripheral reporting obligations. This paper examines the integration of ESG metrics into strategic decision-making, analyzing the managerial, operational, and institutional challenges associated with measurement standardization, data reliability, and performance alignment. By synthesizing literature across strategic management, corporate finance, and sustainability reporting, the study evaluates the opportunities presented by ESG integration, including enhanced risk management, improved stakeholder trust, and long-term competitive advantage. The findings suggest that successful ESG integration requires robust governance frameworks, data-driven performance systems, and cultural alignment between sustainability objectives and strategic priorities.
ESG metrics, strategic decision-making, sustainable finance, corporate governance, stakeholder management, long-term value creation
Corporate Entrepreneurship in Established Firms: Balancing Innovation and Control
Corporate entrepreneurship has emerged as a critical mechanism through which established firms sustain competitiveness in dynamic and uncertain environments. Unlike start-ups, mature organizations must pursue innovation while preserving operational stability, governance structures, and shareholder accountability. This paper examines corporate entrepreneurship in established firms, focusing on how organizations balance innovation-driven exploration with control-oriented exploitation. By synthesizing literature on strategic management, organizational ambidexterity, and innovation governance, the study analyzes structural, cultural, and leadership mechanisms that enable firms to foster intrapreneurship without compromising performance discipline. The findings suggest that successful corporate entrepreneurship depends on ambidextrous organizational design, adaptive control systems, and leadership practices that reconcile risk-taking with strategic coherence. Ultimately, balancing innovation and control is positioned as a dynamic managerial capability essential for long-term value creation.
corporate entrepreneurship, intrapreneurship, organizational ambidexterity, innovation strategy, governance, strategic management
Error-Resilient Quantum Compiler Design for Efficient Qubit Mapping, Gate Optimization, and Noise Mitigation in NISQ-Era Devices,
The rapid evolution of Noisy Intermediate-Scale Quantum (NISQ) devices has intensified the need for robust compilation strategies capable of mitigating hardware-induced errors while maximizing computational efficiency. Quantum compilers play a pivotal role in translating high-level quantum algorithms into hardware-executable instructions, yet they face significant challenges related to qubit connectivity constraints, limited coherence times, and stochastic noise. This paper examines error-resilient quantum compiler design for efficient qubit mapping, gate optimization, and noise mitigation in NISQ-era devices, proposing a framework that integrates topology-aware mapping, adaptive gate synthesis, and probabilistic error suppression techniques. By synthesizing advances in quantum circuit optimization, hardware-aware scheduling, and hybrid classical–quantum error correction strategies, the study evaluates how compiler-level innovations can substantially enhance execution fidelity and resource efficiency. The findings suggest that intelligent compiler architectures are critical enablers of practical quantum advantage during the transitional NISQ phase, bridging theoretical algorithm design with real-world hardware constraints.
quantum compiler, NISQ devices, qubit mapping, gate optimization, noise mitigation, quantum circuit optimization
Quantum Algorithmic Frameworks for Graph-Based Optimization and Network Analysis in Large-Scale Computational Systems
The increasing scale and interconnectedness of modern computational systems have intensified the demand for efficient algorithms capable of analyzing complex networks and solving graph-based optimization problems. Classical optimization techniques, while effective for moderate-sized datasets, often encounter exponential computational growth when applied to large-scale graph structures characterized by high dimensionality and dynamic topology. This paper explores quantum algorithmic frameworks for graph-based optimization and network analysis in large-scale computational systems, emphasizing the potential of quantum computing paradigms to address combinatorial complexity and enhance computational scalability. By synthesizing developments in quantum search algorithms, variational quantum circuits, and hybrid quantum–classical optimization methods, the study evaluates their applicability to tasks such as shortest-path discovery, clustering, community detection, and network resilience assessment. The findings suggest that quantum-enabled frameworks may provide complementary advantages in exploring vast solution spaces and improving optimization efficiency, particularly when integrated with classical preprocessing and orchestration strategies.
quantum algorithms, graph optimization, network analysis, large-scale computation, quantum–classical hybrid models, combinatorial optimization
Efficient Storage and Retrieval Models for Large-Scale Unstructured Data Analytics
The exponential growth of digital information has led to an unprecedented surge in unstructured data originating from sources such as social media, multimedia platforms, sensor networks, and enterprise systems. Traditional relational databases and structured storage frameworks are increasingly inadequate for handling the scale, heterogeneity, and velocity of such data. This paper examines efficient storage and retrieval models for large-scale unstructured data analytics, focusing on distributed architectures, indexing strategies, and intelligent retrieval mechanisms. By synthesizing advances in cloud storage systems, NoSQL databases, vector search techniques, and machine learning–assisted data organization, the study evaluates how modern data infrastructures can optimize performance, scalability, and accessibility. The findings highlight the importance of hybrid storage paradigms and semantic retrieval frameworks in enabling rapid, accurate analysis of massive unstructured datasets, thereby supporting data-driven decision-making across industries.
unstructured data analytics, data storage models, information retrieval, big data systems, distributed databases, semantic indexing
Quantum Machine Learning Architectures for Stock Market Forecasting, Anomaly Detection, and Pattern Recognition in Financial Time Series
Financial markets generate complex, high-dimensional time series characterized by nonlinearity, noise, and structural uncertainty, posing persistent challenges for classical machine learning and statistical forecasting models. This paper examines quantum machine learning (QML) architectures for stock market forecasting, anomaly detection, and pattern recognition in financial time series. By leveraging quantum phenomena such as superposition, entanglement, and quantum-enhanced feature spaces, QML models offer new computational paradigms for capturing intricate market dynamics and latent structures. The study synthesizes recent advances in quantum neural networks, variational quantum circuits, and hybrid quantum–classical models, evaluating their applicability to predictive modeling and risk-sensitive detection tasks in finance. The paper argues that QML architectures have the potential to complement classical approaches by improving representational capacity and scalability for complex financial data, while also outlining current technical limitations and research directions necessary for practical deployment.
quantum machine learning, financial time series, stock market forecasting, anomaly detection, pattern recognition, quantum finance
Quantum-Inspired Modeling of Collective Behavior in Social Networks
Understanding collective behavior in social networks is a central challenge in social science, data science, and computational modeling. Classical network and agent-based models often assume linear interactions and independent decision-making, yet real-world social systems exhibit nonlinear dynamics, rapid opinion shifts, polarization, and context-dependent responses that are difficult to predict. This study explores how quantum-inspired modeling frameworks can enhance the analysis of collective behavior by incorporating principles such as superposition, interference, and probabilistic state transitions. Rather than treating individual opinions as fixed, quantum-inspired approaches model social agents as existing in multiple potential cognitive or behavioral states that evolve through interaction and contextual influence. Through qualitative synthesis of literature in network science, behavioral modeling, and quantum-inspired computation, this paper examines how these frameworks capture emergent phenomena such as opinion cascades, synchronization, and abrupt phase transitions in social dynamics. The analysis highlights applications in misinformation diffusion, social polarization, collective decision-making, and adaptive coordination. The findings suggest that quantum-inspired models offer a flexible and scalable approach for representing uncertainty, ambiguity, and contextual dependence in social systems. Ultimately, this study positions quantum-inspired modeling as a promising tool for advancing the predictive understanding of collective behavior in complex social networks.
collective behavior, social networks, quantum-inspired modeling, opinion dynamics, emergent phenomena, network science, social complexity
Personalized Medicine: How Genetic Differences Affect Drug Responses
Interindividual variability in drug response presents a persistent challenge to effective and safe medical treatment. While conventional pharmacotherapy often relies on standardized dosing regimens, clinical outcomes frequently vary due to underlying genetic differences among patients. This paper examines personalized medicine with a focus on how genetic variation influences drug efficacy, toxicity, and therapeutic optimization. Drawing on advances in genomics, pharmacogenomics, and molecular biology, the study explores how genetic polymorphisms affect drug metabolism, transport, and target interactions. By synthesizing recent research, the paper highlights the clinical relevance of genetic biomarkers in predicting drug response and minimizing adverse drug reactions. The findings underscore the potential of personalized medicine to shift healthcare from population-based treatment paradigms toward individualized therapeutic strategies that improve patient outcomes, enhance drug safety, and support precision healthcare delivery.
personalized medicine, pharmacogenomics, genetic variation, drug response, precision medicine, adverse drug reactions
Digital Brand Building in Entrepreneurial Ventures: The Role of Data-Driven Marketing, UX Design, and Platform Strategy in Customer Acquisition and Loyalty
Digital platforms have fundamentally reshaped how entrepreneurial ventures build brands, acquire customers, and sustain long-term loyalty. This paper examines digital brand building in entrepreneurial ventures, focusing on the strategic interplay between data-driven marketing, user experience (UX) design, and platform strategy. Unlike established firms, entrepreneurial ventures operate under conditions of resource constraints, market uncertainty, and rapid iteration, making digital channels central to brand formation rather than mere communication tools. Drawing on research from digital marketing, human–computer interaction, and platform economics, this study synthesizes how data analytics enable precision targeting, UX design shapes brand perception through user interaction, and platform strategies facilitate scalable customer engagement. The paper argues that successful digital brand building emerges from the alignment of analytical insight, experiential design, and ecosystem-oriented platform thinking. By framing branding as a dynamic, data-informed process embedded in digital infrastructures, this research contributes to a deeper understanding of how entrepreneurial ventures can achieve sustainable customer acquisition and loyalty in competitive digital markets.
digital brand building, entrepreneurial ventures, data-driven marketing, user experience design, platform strategy, customer loyalty
Biophilic Urbanism: Integrating Nature-Based Design Strategies to Enhance Psychological Well-Being in Dense Cities
Rapid urbanization and increasing population density have intensified concerns regarding mental health, environmental stress, and reduced access to nature in contemporary cities. Biophilic urbanism has emerged as a nature-based design paradigm that seeks to reintegrate natural elements and processes into the built environment to enhance psychological well-being and urban livability. This paper examines biophilic urban design strategies within dense urban contexts, focusing on their capacity to mitigate stress, restore cognitive functioning, and foster emotional resilience among urban populations. Drawing on interdisciplinary research from environmental psychology, urban design, and public health, the study synthesizes evidence linking nature-integrated urban spaces to improved mental health outcomes. It further analyzes spatial, architectural, and infrastructural approaches—ranging from green corridors and vertical landscapes to sensory-rich public spaces—that operationalize biophilic principles at multiple urban scales. The paper argues that biophilic urbanism is not merely an aesthetic or ecological intervention but a critical framework for designing psychologically supportive cities in an era of intensifying urban density and environmental uncertainty.
biophilic urbanism, psychological well-being, nature-based design, dense cities, urban mental health, sustainable urban design
Adaptive Architecture and Smart Materials: Designing Responsive Building Envelopes for Climate Resilience
Accelerating climate change has intensified the need for architectural systems that can dynamically respond to environmental variability while reducing energy consumption and enhancing occupant comfort. This paper examines adaptive architecture and smart materials as integrated strategies for designing responsive building envelopes capable of improving climate resilience. Unlike static façades, responsive envelopes leverage material intelligence, embedded sensing, and real-time actuation to regulate heat, light, airflow, and moisture in response to changing climatic conditions. This study synthesizes recent advances in smart materials—such as phase-change materials, shape-memory alloys, and electrochromic systems—and evaluates their architectural integration within adaptive envelope frameworks. By analyzing performance-driven design principles and emerging case studies, the paper demonstrates how responsive envelopes can mitigate thermal stress, reduce operational energy demand, and enhance building longevity under extreme climate scenarios. The findings position adaptive building envelopes not only as technological solutions but also as a paradigm shift in architectural design, redefining the relationship between buildings, users, and their environments in an era of climate uncertainty.
adaptive architecture, smart materials, responsive building envelopes, climate resilience, sustainable design, building performance
Social Media Virality and the Diffusion of Innovation: A Network-Econometric Approach
The rapid expansion of social media platforms has fundamentally transformed the mechanisms through which innovations diffuse across populations. Virality, once considered a stochastic outcome of exposure, has evolved into a structurally mediated, network-driven phenomenon shaped by social interactions and platform algorithms. This study examines social media virality through the theoretical lens of diffusion of innovation, employing a network-econometric framework to capture both individual adoption behavior and systemic propagation effects. By integrating network analysis with econometric modeling, the paper accounts for peer effects, endogenous network formation, and algorithmic amplification mechanisms that influence diffusion dynamics. Social media networks are conceptualized as weighted and dynamic graphs, enabling the analysis of how network characteristics—such as centrality, clustering, and homophily—affect adoption thresholds and diffusion speed. The proposed framework addresses key identification challenges in social interaction models, including simultaneity and correlated unobservables, through structural modeling and instrumental variable strategies. This research contributes to the literature by linking micro-level decision-making to macro-level diffusion patterns and offers implications for innovation strategy, digital platform governance, and causal inference in networked environments.
social media virality, diffusion of innovation, network econometrics, peer effects, digital platforms, innovation adoption
Entrepreneurial Feminism as a Framework for Inclusive Venture Creation and Innovation
Entrepreneurship has traditionally been conceptualized through frameworks that prioritize growth maximization, competitive dominance, and risk-taking norms historically associated with masculine economic models. In response to the structural inequalities embedded in these paradigms, entrepreneurial feminism has emerged as a transformative framework that redefines venture creation, innovation, and risk through the lens of gender equity and inclusion. This paper examines entrepreneurial feminism as both a theoretical and practical approach that challenges dominant entrepreneurial narratives by emphasizing collaborative leadership, social value creation, ethical governance, and inclusive access to resources. Through a multidimensional analysis, the study explores how feminist principles reshape entrepreneurial motivations, redefine risk-taking behaviors, and foster alternative innovation pathways that integrate economic sustainability with social justice. The findings suggest that entrepreneurial feminism not only expands opportunities for women and marginalized groups, but also offers more resilient and equitable models of business creation in contemporary economies.
Entrepreneurial feminism, gender equity, inclusive entrepreneurship, innovation, venture creation, feminist economics
Copyright (c) 2025 Next Generation Journal for The Young Researchers
Quantum Simulation of Brain Network Dynamics in Psychological Disorders
Psychological disorders are increasingly conceptualized as disruptions in large-scale brain network dynamics rather than impairments confined to isolated neural regions. Although classical computational and statistical approaches have advanced the study of neural connectivity, they often fall short in representing the nonlinear, probabilistic, and context-dependent characteristics of brain activity. Quantum simulation offers an alternative modeling framework by employing quantum-inspired mathematical principles—including superposition, entanglement, and interference—to describe complex neural interactions and their temporal evolution. This paper investigates the potential of quantum-inspired simulation techniques to model brain network dynamics associated with psychological disorders such as depression, anxiety, schizophrenia, and neurodevelopmental conditions. By representing neural systems as probabilistic networks capable of occupying multiple functional states simultaneously, quantum-based models provide a powerful means of capturing cognitive variability, network instability, and pathological state transitions. Drawing on insights from neuroscience, quantum cognition, and computational psychiatry, the study highlights how quantum-inspired simulations may contribute to improved mechanistic understanding, predictive modeling, and personalized approaches to mental health treatment.
quantum-inspired simulation, brain network dynamics, psychological disorders, computational psychiatry, neural connectivity, cognitive instability, complex systems
Quantum-Enhanced Genomic Data Analysis for Evolutionary Biology and Species Adaptation Modeling
The exponential growth of genomic data has transformed evolutionary biology, enabling large-scale analysis of genetic variation, phylogenetic relationships, and adaptive mechanisms across species. However, classical computational approaches increasingly struggle with the combinatorial complexity, high dimensionality, and nonlinear interactions inherent in genomic datasets. This study explores how quantum-enhanced data analysis can augment evolutionary biology by enabling more efficient modeling of genomic variation and species adaptation. By leveraging quantum computing principles such as superposition, entanglement, and quantum parallelism, quantum algorithms offer the potential to accelerate sequence alignment, genotype–phenotype mapping, evolutionary optimization, and adaptive landscape exploration. Through qualitative synthesis of research in quantum computing, computational genomics, and evolutionary theory, this paper examines how quantum-enhanced frameworks can improve the detection of adaptive patterns, epistatic interactions, and evolutionary constraints. The analysis highlights potential applications in phylogenomic reconstruction, population genetics, and predictive modeling of species responses to environmental pressures. While acknowledging current technological limitations, the study argues that quantum-enhanced genomic analysis represents a promising pathway toward more scalable, precise, and predictive evolutionary models. Ultimately, this work positions quantum computing as a transformative tool for advancing our understanding of biological evolution and species resilience in rapidly changing ecosystems.
quantum computing, genomics, evolutionary biology, species adaptation, phylogenomics, genotype–phenotype mapping, computational biology, quantum optimization
Quantum Algorithmic Trading: Economic Efficiency, Market Regulation, and Antitrust Law Implications
The rise of quantum computing introduces transformative possibilities for financial markets, particularly in the realm of algorithmic trading. Quantum algorithms—such as Quantum Approximate Optimization Algorithm (QAOA), amplitude amplification, and quantum-enhanced machine learning—promise substantial gains in processing speed, portfolio optimization, arbitrage detection, and real-time market forecasting. However, these technological advantages also raise significant concerns regarding market stability, regulatory oversight, and the preservation of competitive fairness. This study examines how quantum algorithmic trading may alter the economic efficiency of financial markets while simultaneously challenging existing regulatory and antitrust frameworks. Through qualitative analysis of economic theory, legal scholarship, and emerging quantum-finance research, the paper highlights potential risks including informational asymmetry, hyper-accelerated arbitrage cycles, systemic fragility, and the concentration of quantum capabilities among dominant market participants. The findings reveal that while quantum trading technologies may enhance liquidity and price discovery, they also necessitate proactive regulatory modernization to prevent monopolistic behavior, protect smaller market actors, and maintain transparent, resilient trading environments. Ultimately, this research argues that the integration of quantum computing into algorithmic trading represents a critical inflection point that requires coordinated economic, legal, and technological policy responses.
quantum computing, algorithmic trading, market efficiency, financial regulation, antitrust law, quantum optimization, systemic risk, digital markets
Quantum Propensity Modeling of Economic Decision-Making: Capturing Preference Reversals and Contextual Interference
Classical economic decision-making models rely on assumptions of stable preferences, logical consistency, and additive probabilities. Yet decades of behavioral research consistently reveal systematic violations of these principles, including preference reversals, framing effects, and context-sensitive judgment patterns that challenge rational-choice theory. This study examines how quantum propensity modeling—grounded in mathematical structures derived from quantum theory—offers a compelling alternative for understanding these anomalies. Unlike classical models, quantum cognition treats decisions as evolving cognitive states represented in superposition, allowing individuals to hold simultaneous, uncertain predispositions before committing to a choice. Contextual interference, probability amplitude effects, and state-dependent transitions enable these models to accurately describe how information order, framing, and emotional cues distort decision trajectories. Through qualitative analysis of case studies from behavioral economics, cognitive psychology, and decision theory, this research highlights how quantum models capture the non-commutativity of choices, the instability of preferences under uncertainty, and the mechanisms behind cognitive interference patterns. The findings suggest that quantum propensity modeling not only explains well-documented behavioral anomalies more coherently than classical frameworks but also provides a theoretical foundation for applications in market forecasting, algorithmic pricing, consumer behavior analysis, and policy design. Ultimately, the study argues that quantum-inspired decision models represent a transformative step toward more realistic, context-aware interpretations of economic behavior.
quantum cognition, economic decision-making, behavioral anomalies, preference reversals, contextual interference, superposition states, decision dynamics, economic psychology
Quantum Virtualization for Scalable Fintech Workflows: Multi-Tenant qVMs in Quantum Cloud Environments
As financial technology (FinTech) systems evolve toward increasingly sophisticated, data-intensive, and latency-sensitive computational pipelines, quantum computing is emerging as a transformative enabler for optimization, security, and high-performance analytics. However, practical deployment remains limited by scarce quantum hardware, noisy environments, and the need to support heterogeneous users with diverse workloads. This study introduces a quantum virtualization framework that leverages multi-tenant quantum virtual machines (qVMs) to provide scalable, flexible, and secure access to quantum resources in cloud-based environments. The proposed architecture virtualizes quantum hardware through layered abstraction, dynamic qubit allocation, and workload-adaptive circuit scheduling to support concurrent FinTech workloads such as portfolio optimization, fraud detection, blockchain verification, and quantum-safe cryptographic operations. Benchmark simulations demonstrate that qVM-enabled multi-tenancy improves hardware utilization by up to 40%, reduces queueing latency, and enables isolation between concurrent clients without compromising quantum circuit fidelity. These findings highlight the potential of quantum virtualization to accelerate the industrial adoption of quantum computing in the FinTech sector, enabling scalable, secure, and cost-efficient quantum-as-a-service (QaaS) ecosystems.
quantum virtualization; quantum cloud computing, multi-tenant qVMs, FinTech optimization, quantum resource management, quantum-as-a-service, quantum security, scalable workflows
Hybrid Quantum–Classical Optimization for Satellite Constellation Design and Orbital Debris Management
The rapid expansion of satellite constellations and the growing density of orbital debris have intensified the need for advanced computational tools capable of optimizing orbital architectures, collision avoidance, and long-term space sustainability. Traditional optimization methods are increasingly constrained by the combinatorial complexity of constellation design variables—including orbital planes, phasing, revisit requirements, and communication coverage—and the nonlinear dynamics of debris propagation. This study introduces a hybrid quantum–classical optimization framework that integrates quantum approximate optimization algorithms (QAOA), quantum annealing, and classical multi-physics orbital simulation to improve decision-making in constellation deployment and debris mitigation. Quantum subroutines accelerate the search for globally efficient orbital configurations, while classical solvers handle high-fidelity astrodynamics, propagation models, and mission constraints. Preliminary simulation results indicate that the hybrid framework yields more efficient constellation geometries, reduces collision risk, and enhances debris avoidance planning compared to classical baselines. These findings highlight the potential of quantum-assisted optimization to support safer, more resilient, and sustainably managed space systems.
hybrid quantum–classical optimization, satellite constellations, orbital debris mitigation, QAOA, quantum annealing, astrodynamics, space sustainability, collision avoidance
Quantum Computing for Aerothermal and Structural Optimization of Next-Generation Mobility Systems
Next-generation mobility systems—ranging from electric vertical takeoff and landing (eVTOL) vehicles to hypersonic aircraft and high-efficiency autonomous drones—require advanced aerothermal and structural optimization techniques to meet demands for safety, performance, and sustainability. Traditional computational approaches, such as finite element analysis, computational fluid dynamics (CFD), and multi-physics simulation, face increasing computational burdens due to the nonlinear interactions among thermal loads, aerodynamic behavior, material deformation, and structural integrity. This study investigates the role of quantum computing in accelerating and improving aerothermal and structural optimization workflows. Leveraging quantum algorithms, including quantum annealing, variational quantum eigensolvers (VQE), quantum approximate optimization algorithms (QAOA), and quantum-inspired multi-objective solvers, the proposed framework enhances the exploration of high-dimensional design spaces and improves the computational efficiency of complex optimization tasks. Early experiments on benchmark aerothermal–structural models demonstrate improved convergence rates, superior Pareto-optimal solutions, and enhanced predictive accuracy compared to classical methods. The findings show that quantum computing has the potential to fundamentally transform mobility system design by enabling faster iteration cycles, better thermal–structural co-optimization, and more energy-efficient architectures suitable for sustainable transportation futures.
quantum computing, aerothermal optimization, structural optimization, mobility systems, QAOA, VQE, quantum annealing, multi-physics simulation, aerospace engineering
Quantum Algorithms for Lightweight Structural Design and Crash Energy Absorption Optimization
Lightweight structural design requires balancing conflicting objectives: minimizing mass while maximizing crashworthiness, energy absorption, and structural integrity. Conventional optimization techniques—such as gradient-based solvers, evolutionary algorithms, and surrogate modeling—often struggle with the nonlinear, multi-objective, and combinatorial nature of structural configurations, especially in applications like automotive chassis design, aerospace components, and protective systems. This study explores the use of quantum algorithms to accelerate and enhance the optimization of lightweight structures with respect to crash energy absorption. Leveraging quantum annealing, the Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantum–classical variational methods, the framework maps topology, material distribution, and structural geometry to quantum-encoded optimization landscapes. Early simulation results indicate that quantum-enabled solvers can identify higher-performing structural designs and navigate the trade-off between stiffness and crash energy dissipation more efficiently than classical algorithms. These findings highlight the potential of quantum computing to revolutionize structural engineering workflows by delivering faster, more robust, and more scalable optimization for next-generation lightweight designs.
quantum optimization, lightweight structures, crashworthiness, energy absorption, QAOA, quantum annealing, topology optimization, multi-objective design, structural engineering, quantum–classical hybrid models
Quantum Optimization of Energy Consumption in Flexible Manufacturing Systems Integrating Renewable Power Sources
As modern manufacturing transitions toward flexible and energy-adaptive production environments, optimizing energy consumption has become essential for achieving operational sustainability and aligning with global decarbonization goals. Traditional optimization techniques often struggle with the nonlinear, multi-objective, and stochastic nature of energy flows in flexible manufacturing systems, especially when integrating intermittent renewable energy sources such as solar and wind. This study proposes a quantum-enhanced optimization framework leveraging quantum annealing, variational quantum algorithms, and hybrid quantum–classical solvers to minimize energy usage while maintaining production throughput and scheduling feasibility. The approach formulates energy allocation, load balancing, and production scheduling as combinatorial optimization problems that benefit from quantum parallelism and high-dimensional state exploration. Simulations conducted on benchmark manufacturing models demonstrate that the quantum-enabled framework improves peak-load reduction, enhances renewable utilization rates, and reduces overall energy consumption by up to 22% compared to classical baselines. These results indicate the transformative potential of quantum optimization in enabling cleaner, more resilient, and sustainably powered manufacturing ecosystems.
quantum optimization; flexible manufacturing; renewable energy integration; energy-aware scheduling; quantum annealing; variational quantum algorithms; hybrid quantum–classical models; sustainable manufacturing; smart factories
Quantum Machine Learning for Multi-Criteria Decision-Making in Industrial Project Portfolio Management
Industrial project portfolio management (PPM) increasingly relies on complex, multidimensional decision-making processes that must balance financial constraints, risk profiles, resource limitations, sustainability targets, and strategic priorities. Traditional analytical and machine learning methods struggle to scale efficiently as portfolio complexity increases, particularly when decisions involve nonlinear interactions, high-dimensional criteria, and combinatorial optimization. This study explores the use of quantum machine learning (QML) frameworks to enhance multi-criteria decision-making (MCDM) in industrial PPM. By leveraging quantum-enhanced feature spaces, variational quantum classifiers, and hybrid quantum–classical optimization methods, the approach enables improved evaluation of project alternatives, more robust prioritization under uncertainty, and more efficient exploration of exponentially large decision spaces. The research demonstrates how QML-driven MCDM architectures can support dynamic portfolio balancing, scenario-based investment planning, and real-time reallocation strategies in fast-evolving industrial environments. The findings highlight the potential for quantum machine learning to shape the next generation of intelligent portfolio decision-support systems.
quantum machine learning, multi-criteria decision-making, project portfolio management, industrial optimization, hybrid quantum–classical algorithms, decision analytics
Embedding-Based Quantum Simulation of Complex Energy Systems for Smart Grid and Sustainable Engineering Applications
The increasing complexity of modern energy systems—driven by distributed renewable generation, dynamic grid behavior, and multi-scale interactions—necessitates computational models capable of capturing nonlinearities, stochastic variability, and high-dimensional optimization landscapes. Embedding-based quantum simulations offer a promising path forward by integrating quantum solvers into classical energy modeling frameworks, enabling more accurate analysis of localized subsystems while maintaining tractable large-scale system evaluations. This study investigates quantum embedding techniques applied to power flow analysis, renewable integration, grid stability assessment, and energy storage optimization. By partitioning complex energy networks into quantum-treated active regions embedded within classical architectures, the approach enhances the fidelity of system-level simulations while reducing computational overhead. The research highlights the potential of hybrid quantum–classical workflows to accelerate decision-making in smart grid design, improve forecasting under uncertainty, and support the development of sustainable engineering solutions. The results outline a roadmap for deploying quantum-embedded simulations in future energy infrastructures.
quantum embedding, smart grid, sustainable energy systems, quantum simulation, grid optimization, renewable integration, hybrid quantum–classical computation
Quantum-Enhanced Computational Catalysis: Optimizing Reaction Pathway Simulations with Advanced Tensor Decomposition Techniques
Quantum-enhanced computational catalysis offers a transformative framework for modeling complex reaction pathways with unprecedented accuracy and scalability. As catalytic processes often involve strongly correlated electrons, multidimensional potential energy surfaces, and dynamic coupling between nuclear and electronic degrees of freedom, classical simulation methods face steep computational barriers. This study explores a hybrid quantum–classical workflow that integrates quantum algorithms for electronic structure with advanced tensor decomposition techniques to compress, analyze, and optimize high-dimensional reaction data. By leveraging tensor-network representations—such as matrix product states, tensor-train formats, and hierarchical Tucker decompositions—the approach systematically reduces computational cost while maintaining chemically relevant accuracy. The research demonstrates how tensor-optimized quantum simulations can accelerate transition-state discovery, enhance reaction pathway resolution, and improve predictions of catalytic efficiency across heterogeneous, homogeneous, and photocatalytic systems. The synergy between quantum solvers and tensor decomposition establishes a powerful platform for next-generation catalytic modeling and materials discovery.
quantum catalysis, reaction pathway simulations, tensor decomposition, tensor networks, electronic structure, quantum algorithms, catalytic mechanisms, computational chemistry
Quantum Computational Models for Photoelectrochemical Processes in Solar Fuel and Energy Conversion Systems
Quantum computational models are emerging as powerful tools for understanding and optimizing photoelectrochemical (PEC) processes that underpin next-generation solar fuel and energy conversion systems. These systems involve complex interactions between photons, charge carriers, catalytic surfaces, and molecular intermediates, making accurate predictive modeling challenging for classical simulation frameworks. This study investigates quantum-enabled approaches for simulating light–matter interactions, exciton dynamics, charge-transfer pathways, and catalytic reaction coordinates within PEC architectures. By leveraging quantum algorithms for electronic structure, nonadiabatic dynamics, and reaction energetics, the research explores how quantum simulations can resolve strongly correlated states, multi-electron excitations, and interfacial charge separation with higher fidelity. The analysis highlights the potential of quantum models to accelerate the discovery of efficient photoelectrode materials, enhance solar-to-fuel conversion efficiencies, and support the rational design of artificial photosynthesis systems. Finally, the paper discusses the integration of quantum computational predictions with experimental PEC workflows and outlines opportunities for future hybrid quantum–classical research.
quantum simulation, photoelectrochemistry, solar fuels, energy conversion, light–matter interactions, electronic structure, charge transfer, artificial photosynthesis, quantum chemistry
Embedding-Based Quantum Simulations for Multi-Scale Modeling of Chemical Systems
Embedding-based quantum simulations have emerged as a promising paradigm for bridging quantum and classical scales in complex chemical systems. As molecular processes span multiple spatial and temporal regimes—from localized electron correlation to mesoscale reactivity—traditional quantum chemistry and classical potentials often fail to describe these interactions with sufficient accuracy and scalability. This study explores hybrid embedding frameworks that integrate quantum subspace models, density functional embedding, and quantum many-body solvers within hierarchical multi-scale workflows. By partitioning chemically active regions and treating them with quantum processors while embedding them within larger classical environments, the approach improves accuracy in reaction barrier estimation, excited-state dynamics, and strongly correlated interactions. We evaluate state-of-the-art quantum embedding techniques, discuss advances in quantum resource allocation and error mitigation, and demonstrate how embedding can reduce computational depth while preserving chemically essential features. The study highlights key opportunities for developing next-generation quantum-enhanced multi-scale simulation pipelines capable of accelerating catalyst design, energy storage materials, and biochemical modeling.
quantum embedding, multi-scale modeling, quantum chemistry, electronic structure, density functional embedding, hybrid quantum-classical algorithms, strongly correlated systems, chemical simulations