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 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
Copyright (c) 2025 Next Generation Journal for The Young Researchers
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
Comprehensive Review of Quantum Computing Applications in Finance: Derivative Pricing, Risk Management, and Portfolio Optimization
Quantum computing has emerged as a transformative computational paradigm, offering novel capabilities to tackle complex problems in finance—most notably in derivative pricing, risk management, and portfolio optimization. This review synthesizes recent advances in quantum algorithms and hybrid quantum–classical frameworks as applied to financial services, examining how technologies such as quantum-annealing, quantum amplitude estimation, and variational quantum circuits can improve efficiency, accuracy, and scalability over classical methods. The study provides a systematic assessment of key use-cases: (1) derivative pricing and greeks computation via quantum Monte Carlo and amplitude estimation; (2) risk management enhancements, including Value-at-Risk (VaR) and conditional VaR through quantum gradient estimation; and (3) portfolio optimization via QUBO (quadratic unconstrained binary optimization) mapping, the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing approaches. In addition to reviewing technological potential, the paper addresses critical barriers—hardware noise, coherence limitations, data integration, regulatory considerations—and outlines future research directions, including quantum-resilient cryptography in finance and industry coupling. By bringing together algorithmic, financial and regulatory perspectives, this review seeks to guide academics and practitioners toward the next frontier in financial computing.
quantum computing, finance, derivative pricing, risk management, portfolio optimization, quantum annealing, quantum amplitude estimation, QAOA, hybrid quantum–classical systems
Cross-Disciplinary Integration of Quantum Computing in Economics and Finance
The convergence of quantum computing and economics represents a frontier in computational social science, promising to transform the modeling of uncertainty, complexity, and interdependence in financial and macroeconomic systems. This study explores how quantum computing methodologies—particularly quantum annealing, variational algorithms, and hybrid quantum–classical frameworks—can be integrated into Dynamic Stochastic General Equilibrium (DSGE) modeling, stochastic simulations, and systemic risk analytics. By focusing on collaborative university–industry partnerships, the paper examines mechanisms that accelerate translational research, facilitate scalable experimentation, and bridge theoretical quantum advances with applied financial modeling. The analysis highlights how quantum-enhanced algorithms can improve the tractability and precision of multidimensional optimization problems inherent in macroeconomic forecasting and risk estimation. It also emphasizes the institutional value of joint academic–industrial infrastructures in fostering data sharing, interdisciplinary education, and early adoption of post-classical computing in finance. The findings suggest that hybridized quantum frameworks can substantially advance decision sciences, enabling resilient and adaptive economic systems capable of responding to global shocks with greater computational foresight.
Quantum computing, DSGE models, stochastic simulations, systemic risk, hybrid algorithms, university–industry collaboration, financial modeling, computational economics
Quantum Algorithms for Portfolio Optimization in Finance
The increasing complexity of financial markets has heightened the demand for advanced computational methods capable of optimizing portfolios under multi-dimensional constraints involving risk, return, diversification, and liquidity. Traditional optimization frameworks, such as mean-variance analysis and heuristic meta-optimization, are limited by their scalability and sensitivity to non-convex, high-dimensional data structures. This study explores the application of quantum algorithms to portfolio optimization, demonstrating how Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, and Variational Quantum Eigensolver (VQE) can address computational bottlenecks inherent in classical approaches. Quantum computation offers the potential to encode complex financial systems as Hamiltonian functions, allowing parallel exploration of vast solution spaces through superposition and entanglement. This enables faster convergence toward optimal portfolio allocations while simultaneously accounting for multi-factor correlations and dynamic market conditions. The research further investigates the role of hybrid quantum–classical optimization pipelines, in which quantum solvers perform core optimization tasks while classical systems manage data preprocessing and constraint validation. Empirical modeling and simulation indicate that quantum-based approaches can outperform conventional methods in risk-adjusted return optimization, efficient frontier computation, and real-time portfolio rebalancing. By bridging the gap between quantum computing and financial engineering, this study contributes to the development of next-generation financial analytics systems, aligning with the broader goal of achieving more adaptive, transparent, and data-resilient financial decision-making.
quantum algorithms, portfolio optimization, quantum annealing, QAOA, financial engineering, risk management, hybrid quantum–classical computation, asset allocation
Sustainable Business Model Innovation: The Role of Circular Economy Principles in the Digital Age
The accelerating pace of digital transformation is reshaping how organizations conceptualize and implement sustainability within their business models. This study explores how circular economy (CE) principles—such as resource efficiency, product longevity, and waste valorization—can be embedded into digital business model innovation (BMI) to foster economic resilience and environmental stewardship. Through an integrative framework combining digital technologies (AI, IoT, blockchain) with CE strategies, the research investigates how firms transition from linear value creation to regenerative, data-driven ecosystems. The study also highlights the enabling role of digitalization in material tracking, predictive maintenance, and closed-loop supply chains, emphasizing its potential to decouple growth from resource depletion. Findings suggest that sustainable business model innovation in the digital era requires not only technological adaptation but also organizational learning, stakeholder collaboration, and systemic thinking.
Sustainable business models, circular economy, digital transformation, innovation, Industry 4.0, ESG integration, resource efficiency
Simulating a Quantum Dot Thermometer using Feynman’s Vector Model for Two-Level Systems
This research aims to demonstrate that Feynman’s vector model can be effectively used to simulate a quantum dot thermometer for nanoscale temperature measurement. Combining theoretical quantum principles with computational modeling, we model quantum dots as precessing vectors on a Bloch sphere, linking excited-state and ground-state population dynamics to temperature via the Boltzmann distribution. Photoluminescence intensity gives the primary temperature readout. Simulations confirm the model’s accuracy at the nanoscale, demonstrating that Feynman’s geometrical approach simplifies complex quantum problems and provides a promising basis for future applications such as targeted drug delivery or cellular thermal monitoring.
Quantum Approximate Optimization Algorithm for Large-Scale Portfolio Selection with Multi-Factor Constraints
Large-scale portfolio optimization is hard because investors must balance risk, return, diversification, and liquidity across many assets with complex constraints. Classical methods, such as quadratic programming or metaheuristics, often become slow or less effective as the number of assets and factors grows. In this project, I explore the use of the Quantum Approximate Optimization Algorithm (QAOA) as a hybrid quantum–classical approach to portfolio selection. The framework encodes portfolio decisions as qubits and represents financial objectives—such as mean–variance trade-offs or risk-adjusted return—inside a parameterized quantum circuit. Classical optimization is then used to tune the circuit parameters so that the quantum system converges toward low-risk, high-return portfolios. Multi-factor constraints, including sector limits, liquidity rules, ESG scores, and macroeconomic risk factors, are integrated into the Hamiltonian as penalty terms. A qualitative comparison with classical solvers (e.g., mixed-integer programming and simulated annealing) suggests that QAOA can scale more gracefully in high-dimensional settings and handle complex constraint structures. Overall, the project shows how quantum hybrid optimization could support future portfolio management, linking theoretical quantum algorithms with practical, sustainability-aware investment strategies.
Resilience Analysis of Clearing and Settlement Systems Under Quantum-Enabled Cybersecurity Threats
Clearing and settlement systems are the hidden core of global finance. They make sure trades are finalized, payments are delivered, and counterparty risk is controlled between large institutions. As quantum computing develops, these systems face new cybersecurity risks, especially against the cryptography that protects messages, keys, and digital signatures. In this project, I develop a resilience analysis framework for clearing and settlement systems under quantum-enabled attack scenarios. The framework maps technical components, operational dependencies, and cryptographic touchpoints, and then studies how quantum attacks—such as harvest-now-decrypt-later strategies, breaking public-key schemes, or tampering with validation messages—could spread through the system. Using ideas from financial market infrastructure regulation and quantum security research, I identify pressure points where cryptographic failure could trigger liquidity shocks or settlement delays. Based on these findings, I outline a layered defence model that combines post-quantum cryptography, cryptographic agility, real-time monitoring, and strong governance and incident-response planning. The main goal is to keep transaction finality, data integrity, and market stability even when quantum-capable attackers appear. The project offers practical guidance for central banks, clearing houses, and payment processors preparing for a quantum-resilient financial ecosystem.
Quantum-Enhanced Optimization of Smart Contract Execution for Automated Financial Services
Decentralized finance (DeFi) uses smart contracts to automate payments, lending, and asset management, but current blockchains often suffer from slow, expensive, and energy-hungry execution. In this project, I explore a quantum-enhanced optimization framework for smart contract–based financial services. The main idea is to treat gas use, transaction ordering, and resource allocation as optimization problems that can be tackled by hybrid quantum–classical algorithms. Using a conceptual model, I map smart contract execution to cost functions suitable for the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). I then compare, at a qualitative level, how these quantum-inspired approaches differ from classical heuristics in terms of expected throughput, latency, and cost. A focused literature review on quantum computing, blockchain scalability, and quantum-safe cryptography provides context for these ideas. The results suggest that quantum-enhanced optimization could reduce gas fees, improve transaction scheduling, and support more efficient consensus under heavy load. The project also discusses the need for post-quantum security so that future quantum computers do not undermine blockchain trust. Overall, the work outlines how quantum computing might contribute to faster, safer, and more sustainable automated financial systems.
Design of Functional Nanomaterials for Energy Storage Systems
The rapid global transition toward renewable energy and electrification demands advanced energy storage systems (ESS) that are efficient, durable, and environmentally sustainable. This research explores the design principles and synthesis strategies of functional nanomaterials for next-generation batteries and supercapacitors, emphasizing how nanoscale engineering can enhance energy density, charge transfer kinetics, and material stability. Conducted as a qualitative investigation, the study examines academic literature and industrial reports to identify trends in nanostructured materials—including metal oxides, carbon-based nanocomposites, and two-dimensional materials—that significantly improve electrochemical performance in modern energy devices. The analysis focuses on how surface morphology, porosity, and chemical composition influence key parameters such as ion diffusion, conductivity, and capacity retention. By mapping correlations between structure and function, the study elucidates the mechanisms through which nanomaterials optimize charge transport pathways and mechanical resilience under high cycling conditions. Additionally, it assesses the socio-economic and environmental implications of nanomaterial-based energy storage, considering factors like resource sustainability, recyclability, and scalability in industrial production. Through interdisciplinary synthesis, the research connects materials science, renewable energy engineering, and clean technology innovation, offering conceptual frameworks that illustrate how functional nanomaterials can contribute to low-carbon energy infrastructures. The findings highlight that intelligent nanomaterial design is central not only to enhancing energy storage efficiency but also to advancing the broader goal of sustainable technological progress.
functional nanomaterials, energy storage systems, batteries, supercapacitors, charge transfer, sustainability, nanostructure design, renewable energy
Integrated Quantum Photonic Architectures for High-Throughput Simulation and Optimization of Industrial Manufacturing Processes
The integration of quantum photonic architectures into industrial computation represents a transformative approach to addressing the growing complexity, precision demands, and energy inefficiencies of modern manufacturing systems. This research explores the development and application of integrated quantum photonic platforms for high-throughput simulation and optimization, enabling large-scale, parallelized modeling of manufacturing processes. Unlike traditional electronic-based computation, photonic architectures exploit the quantum properties of light—such as superposition, entanglement, and interference—to achieve massive computational concurrency with low latency and high energy efficiency. The study focuses on applying quantum photonics to enhance industrial process optimization, including workflow scheduling, supply chain coordination, defect detection, and material design optimization. Through simulations and conceptual modeling, the research demonstrates how photon-based qubits and integrated optical circuits can execute optimization algorithms more efficiently than classical architectures when managing large, interdependent datasets. The findings underscore the potential of quantum-enhanced photonic computing to achieve scalable, real-time decision-making in sectors such as semiconductor fabrication, automotive manufacturing, and energy systems engineering. Conducted in collaboration with academic scholars, the project provided hands-on exposure to algorithmic modeling, quantum heuristic design, and analytical evaluation, deepening the researcher’s understanding of both theoretical and applied quantum systems. Additionally, the experience cultivated transferable skills in critical thinking, interdisciplinary collaboration, and scientific communication—core competencies essential for the next generation of engineers and scientists navigating the quantum industrial era. This study ultimately highlights quantum photonics as a foundational enabler of intelligent, high-performance, and sustainable manufacturing ecosystems.
quantum photonics, integrated photonic circuits, industrial optimization, high-throughput simulation, quantum-enhanced manufacturing, scalability, quantum optics, computational efficiency
Design Automation of Quantum Circuits for Engineering Systems: Gate Optimization and Noise-Aware Compilation in NISQ-Aware Hardware
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.
quantum circuit design, NISQ devices, gate optimization, noise-aware compilation, design automation, quantum transpilation, hybrid computation, quantum-classical co-design
Quantum Computing–Enhanced Service Ecosystems for Industrial Manufacturing Simulation and Workflow Optimization
The advent of quantum computing is redefining the computational landscape of industrial manufacturing, introducing new paradigms for simulation, optimization, and adaptive decision-making. This study investigates the integration of quantum computing–enhanced service ecosystems within industrial manufacturing frameworks, focusing on how quantum algorithms can accelerate workflow optimization, supply chain coordination, and process simulation in large-scale production environments. Traditional simulation and optimization techniques often struggle with the combinatorial complexity of modern manufacturing systems, leading to inefficiencies in resource allocation, scheduling, and predictive maintenance. The proposed framework leverages hybrid quantum–classical computation to simulate high-dimensional manufacturing workflows through quantum annealing, Quantum Approximate Optimization Algorithm (QAOA), and quantum-inspired reinforcement learning. These techniques enable more efficient exploration of multi-objective optimization spaces, facilitating near-real-time decision support and improved throughput under uncertain production conditions. The model also emphasizes the development of service-oriented architectures that integrate quantum computation with industrial Internet of Things (IIoT) infrastructures, digital twins, and cloud-based manufacturing services. Results from conceptual modeling and early simulation experiments suggest that quantum-enhanced service ecosystems outperform conventional digital twins and heuristic optimization methods in adaptability, precision, and energy efficiency. This convergence of quantum computing and industrial systems marks a shift toward next-generation intelligent manufacturing, capable of autonomous learning, dynamic reconfiguration, and sustainable production optimization.
quantum computing, service ecosystems, industrial manufacturing, workflow optimization, digital twins, quantum annealing, hybrid computation, smart industry, quantum optimization
The Role of Epigenetic Modifications in Evolution: Understanding How Environmental Factors Shape Heritable Gene Expression Across Generations
Epigenetic modifications provide a mechanism through which environmental factors can induce heritable changes in gene expression without altering the underlying DNA sequence. While the Modern Synthesis emphasises genetic mutation and natural selection as the primary drivers of evolution, growing evidence highlights the role of DNA methylation, histone modifications, and non-coding RNAs in enabling phenotypic plasticity and transgenerational inheritance. These mechanisms allow organisms to respond to environmental conditions more rapidly than genetic changes alone would permit, and in some cases, these responses are transmitted across generations via the germline. Empirical studies from both plant and animal systems demonstrate how exposures to stress, nutritional changes, and chemical disruptors can leave stable epigenetic marks. Additionally, large-scale genomic rearrangements such as chromoanagenesis point to the potential for abrupt structural variation to interact with epigenetic processes. Incorporating such findings within the framework of the Extended Evolutionary Synthesis expands traditional evolutionary models, offering a more integrated understanding of how environmental stimuli shape heritable variation and influence evolutionary trajectories over both short and long timescales.
epigenetics, gene expression, DNA methylation, histone modification, non-coding RNA, phenotypic plasticity, transgenerational inheritance, environmental stress, chromoanagenesis, extended evolutionary synthesis
The Impact of Workplace Diversity and Inclusion on Work-Life Balance
This study examines the association between diversity and work-life balance. Although it is known that workplace diversity can cause conflict and is referred to as an unfavorable issue, many studies have shown that diversity enhances work performance and work-life balance. In this article, I analyze multiple aspects of diversity considering its reflections in workplaces. The fact that people have different social characteristics indicates that companies need to update their inclusion policies. As companies embrace and internalize diversity, a positive impact is created in the career development and overall lives of employees in the workplace.
diversity, work-life balance, workplace diversity, inclusion policies, employee well-being, career development, workplace conflict, organizational culture, workforce inclusion, employee performance, workplace equity
Copyright (c) 2024 Next Generation Journal for The Young Researchers
The Impact of Diversity and Inclusion on Organizational Culture
Diversity and inclusion (D&I) have become central to modern organizational culture, shaping workplace dynamics, innovation, and overall business performance. A diverse workforce—encompassing differences in race, gender, age, background, and thought—fosters creativity, improves problem-solving, and enhances employee engagement. However, diversity alone is insufficient without an inclusive culture that ensures equitable opportunities, psychological safety, and a sense of belonging for all employees. Organizations that actively integrate D&I initiatives benefit from improved collaboration, greater adaptability in global markets, and stronger brand reputation. Yet, challenges such as unconscious bias, resistance to change, and performative inclusivity can hinder progress. This paper explores how D&I influences organizational culture, examining both the benefits and challenges through the lenses of leadership, policy implementation, and social psychology. By synthesizing research from management studies, behavioral science, and case studies of successful (and unsuccessful) corporate D&I efforts, this study provides a nuanced understanding of how organizations can build truly inclusive workplaces that enhance employee well-being and long-term business sustainability.
Diversity and Inclusion, Organizational Culture, Workplace Equity, Leadership, Employee Engagement, Innovation, Psychological Safety, Unconscious Bias, Corporate Social Responsibility
The Role of Cognitive Biases in Financial Decision-Making
Financial decision-making is often assumed to be a rational process guided by logical assessments of risk and reward. However, research in behavioral finance has consistently demonstrated that cognitive biases—systematic deviations from rational judgment—play a crucial role in shaping investment behavior, corporate decision-making, and personal financial choices. Biases such as overconfidence, loss aversion, anchoring, and herd mentality influence how individuals and organizations perceive financial information and respond to uncertainty. These biases can lead to suboptimal decisions, speculative bubbles, and market inefficiencies. Understanding the psychological underpinnings of financial behavior is essential for investors, policymakers, and financial professionals aiming to mitigate risks associated with irrational decision-making. This paper explores the most prominent cognitive biases affecting financial decisions, their implications for market behavior, and strategies for minimizing their adverse effects. By integrating insights from behavioral economics, psychology, and finance, this study highlights the importance of recognizing and addressing these biases to foster more informed and stable financial markets.
Cognitive Biases, Behavioral Finance, Decision-Making, Risk Perception, Overconfidence, Loss Aversion, Anchoring, Herd Mentality, Market Inefficiencies
Production of Algae-Based Carbon Black Pigment and Its Use in Carbon Neutral Printing Technologies
Within the scope of the project, we focused on the innovation potential in printing technologies, which is a common problem for all sectors, taking into account the contribution of chemistry to sustainability in innovation-oriented sectors. Based on the importance of microalgae in biomass, we obtained biochar for the production of carbon black pigment. Biochar provided an important result especially in the production of a pigment that has the potential to replace petroleum-derived carbon black pigment from fresh and salty algae such as Spirulina and Nannochloropsis. By using the raw pigment material we obtained together with environmentally friendly vegetable resins, we turned it into a product with low volatile organic compounds and no heavy metals. In this context, we examined the potential of the product formed in many dierent sub-elds of biochar printing technologies such as direct printing, UV curing and printing. With biocompatibility experiments, we also examined the status of the project in terms of human and environmental health. Thus, we have obtained preliminary findings on the transition from biomass to printing technologies, which has the potential for an important sectoral change. It will be possible to obtain more qualified and comprehensive products with future experiments. This transformation has the potential to affect many stakeholders for production and consumption.
Establishing a Relationship Between Inequality in Education and Inequality in Income
In this paper the relationship between inequality in education and inequality in the rate of income is explored. Many people from all round the world do not have access to quality education, which is a huge component of the Human Development Index (HDI). HDI is a summary measure of success in several crucial dimensions such as, long and healthy life, being knowledgeable, and the standard of living. Education, therefore, is a key component of the HDI as it is concerned with the standard of living as well as knowledge. Access to education is limited all around the world for a variety of reasons: gender inequality, income, lack of resources etc. Especially in the countries that are relatively poor (mostly in Sub-Saharan Africa) experience major obstacles in education inequality. In this sense, poverty status is correlated with the education gaps, especially when a country is facing severe problems in the distribution of income. In addition, residency in terms of rural or urban areas also does impact education inequality. Although many factors such as geographical location, education inequality, lack in research and development; the focus of the research is the correlation between education inequality and inequality in income distribution.
The Role of AI in Transforming Labor Markets: Productivity Gains Versus Job Displacement
Artificial Intelligence (AI) is reshaping labor markets globally, acting as a dual-edged force of transformation. While it promises unprecedented productivity gains and economic growth, it simultaneously raises concerns about job displacement and workforce polarization. This research examines the role of AI in transforming labor markets, analyzing its impact across industries and demographic groups. By evaluating case studies and leveraging econometric modeling, the study highlights the dichotomy between automation-driven efficiencies and the socio-economic challenges of technological displacement. Key findings reveal the potential for AI to enhance productivity, drive innovation, and create new job categories, while also exacerbating skill mismatches and income inequality. The study underscores the critical importance of proactive policies, including re-skilling initiatives, equitable AI adoption frameworks, and collaborative efforts between stakeholders, to mitigate adverse effects and ensure inclusive growth in an AI-driven economy.
The Economic Implications of Data Monetization in a Digital-First Economy
The digital-first economy has catalyzed an unprecedented shift in the valuation and utilization of data as a primary economic asset. This research delves into the multifaceted economic implications of data monetization, focusing on its role as a pivotal driver of growth, innovation, and policy development. As businesses increasingly leverage data to create new revenue streams, the study examines the evolving dynamics of value creation, competition, and inequality. It investigates the interplay between data-centric business models, market structures, and consumer privacy concerns, offering insights into how regulatory frameworks must adapt to balance innovation with ethical considerations. By analyzing case studies and employing econometric modeling, this research underscores the transformative potential of data monetization while addressing its societal and economic risks. Key findings highlight the necessity for equitable data-sharing mechanisms and global regulatory cooperation to prevent monopolistic tendencies and ensure inclusive economic benefits.