Quantum-computing application studies often stop at the implementation of an isolated circuit or the solution of a small demonstration problem. Industrial adoption requires a more complete engineering process involving problem formulation, quantum encoding, algorithm selection, execution, verification, resource estimation, comparison with strong classical baselines, and assessment of operational relevance. This paper presents an application-oriented framework for QForge, extending its role as a quantum-computing design, emulation, verification, and algorithmdevelopment platform. The framework maps quantum and hybrid methods—including the Quantum Approximate Optimization Algorithm, Variational Quantum Eigensolver, Grover search, Shor period finding, quantum kernels, and amplitude-estimation extensions—to six application classes: industrial optimization, logistics and energy systems, molecular simulation, financial modelling, cybersecurity, quantum machine learning, and education. To reduce premature or misleading application claims, the paper introduces a six-stage evidence model called the QForge Application Readiness Levels (QARL), progressing from problem identification to operational validation. It also defines a reproducible application gallery, classical-baseline discipline, end-to-end time accounting, and structured evidence reports. The paper deliberately distinguishes classical emulation from physical quantum execution and does not claim quantum advantage. Instead, QForge is positioned as an engineering environment in which application hypotheses can be formulated, verified, benchmarked, and prepared for future hybrid high-performance and quantum-computing infrastructures.
Quantum-computing application studies often stop at the implementation of an isolated circuit or the solution of a small demonstration problem. Industrial adoption requires a more complete engineering process involving problem formulation, quantum encoding, algorithm selection, execution, verification, resource estimation, comparison with strong classical baselines, and assessment of operational relevance. This paper presents an application-oriented framework for QForge, extending its role as a quantum-computing design, emulation, verification, and algorithmdevelopment platform. The framework maps quantum and hybrid methods—including the Quantum Approximate Optimization Algorithm, Variational Quantum Eigensolver, Grover search, Shor period finding, quantum kernels, and amplitude-estimation extensions—to six application classes: industrial optimization, logistics and energy systems, molecular simulation, financial modelling, cybersecurity, quantum machine learning, and education. To reduce premature or misleading application claims, the paper introduces a six-stage evidence model called the QForge Application Readiness Levels (QARL), progressing from problem identification to operational validation. It also defines a reproducible application gallery, classical-baseline discipline, end-to-end time accounting, and structured evidence reports. The paper deliberately distinguishes classical emulation from physical quantum execution and does not claim quantum advantage. Instead, QForge is positioned as an engineering environment in which application hypotheses can be formulated, verified, benchmarked, and prepared for future hybrid high-performance and quantum-computing infrastructures.
Quantum computing is transitioning from an academic discipline into an engineering technology. Yet despite substantial global investment in quantum hardware, the software ecosystem remains fragmented and mismatched to that transition. The dominant frameworks were built for physicists writing low-level quantum programs, and they typically depend on external cloud hardware. They are poorly suited to engineering design, industrial deployment, education at scale, and large-scale algorithm development. QForge™ was developed to address this gap. QForge is not another quantum programming library. It is an engineering-oriented quantumdesign environment in which users construct, analyse, optimise, simulate, verify and document quantum systems within a single unified workflow. It integrates visual circuit design, a multiengine simulation architecture, intelligent execution routing, engineering resource estimation, multi-level verification and automated documentation — the quantum analogue of what electronic design automation provides to classical hardware engineering. Three properties distinguish it. It is an engineering environment, not a library. The primary artefact is a circuit on a canvas, not code that generates a circuit. A complete workflow — design, validate, simulate, verify, estimate, report, export — is achievable without writing a line of anything. Code export exists as an exit ramp for users who want it, never as an entry requirement. It routes execution intelligently across multiple engines. Rather than relying exclusively on brute-force state-vector simulation, QForge analyses circuit structure and selects the appropriate computational method automatically. Exact simulation is used wherever feasible; specialised analytical methods are invoked for structured algorithms. Exact QAOA expectation values have been validated at 48 qubits through causal-cone decomposition, and a reduced-space engine handles Grover search on problem spaces intractable under naivedense simulation. Vectorised kernel engineering has delivered 26 ×–79 × speedups over the platform’s reference implementation. It never overstates what it computed.Every result carries explicit method provenance. Exact results are labelled exact and can be independently cross-checked. Approximations are labelled as approximations and carry their error characteristics. Where the platform cannot deliver what was asked, it says so rather than returning something adjacent. QForge runs entirely on commodity hardware with no quantum backend, no cloud account and no network connection — making it deployable in ordinary teaching laboratories, in low-resourceinstitutions, and in air-gapped government and defence environments.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
Quantum computing serves as a revolutionary paradigm in computing, harnessing quantum mechanics principles such as quantum entanglement and superposition for optimization simulation, machine learning, and other problems that are a challenge to classical computer systems. The article will examine different usage opportunities in retail and supply chain operations, healthcare and life sciences domains, insurance operations, and financial service industries. Better computation can be achieved with quantum algorithms in discrete and continuous optimization, logistics network optimization, warehouse management, and product delivery optimized by quantum algorithms. Application domains in healthcare include simulation of molecules for medical breakthroughs, genomic analysis, and medical studies optimized by quantum eigenvalue algorithms and quantum eigensolver algorithms. The insurance industry portfolio targets catastrophic modeling, identification, and risk assessment optimized by quantum amplitude estimation algorithms and quantum machine learning algorithms. Financial service industries include derivative pricing, portfolio optimization, credit risk evaluation, and real-time risk calculation optimized in quantum algorithms. The path towards achieving quantum benefit in quantum technology involves using noisy intermediate quantum technology with hybrid quantum and classical designs and error management schemes, where integration opportunities for different industries arise from their common computation problem schemas in need of further scaling in hardware technology.
Quantum computing serves as a revolutionary paradigm in computing, harnessing quantum mechanics principles such as quantum entanglement and superposition for optimization simulation, machine learning, and other problems that are a challenge to classical computer systems. The article will examine different usage opportunities in retail and supply chain operations, healthcare and life sciences domains, insurance operations, and financial service industries. Better computation can be achieved with quantum algorithms in discrete and continuous optimization, logistics network optimization, warehouse management, and product delivery optimized by quantum algorithms. Application domains in healthcare include simulation of molecules for medical breakthroughs, genomic analysis, and medical studies optimized by quantum eigenvalue algorithms and quantum eigensolver algorithms. The insurance industry portfolio targets catastrophic modeling, identification, and risk assessment optimized by quantum amplitude estimation algorithms and quantum machine learning algorithms. Financial service industries include derivative pricing, portfolio optimization, credit risk evaluation, and real-time risk calculation optimized in quantum algorithms. The path towards achieving quantum benefit in quantum technology involves using noisy intermediate quantum technology with hybrid quantum and classical designs and error management schemes, where integration opportunities for different industries arise from their common computation problem schemas in need of further scaling in hardware technology.
The integration of quantum computing and artificial intelligence (AI) constitutes a bidirectional synergy that is reshaping both disciplines. This review investigates the reciprocal relationship in which AI addresses foundational challenges in quantum computing, while quantum computing offers the potential to advance machine learning beyond classical constraints. Recent advancements exemplify this interaction: Google DeepMind’s AlphaQubit neural network decoder has achieved state-of-the-art quantum error correction, improving performance by 6% over tensor networks and 30% over correlated matching methods. Additionally, quantum neural networks have demonstrated exponential improvements in sample complexity for specific learning tasks. This review systematically examines the current landscape of quantum-AI integration across three primary dimensions. First, it addresses AI-enhanced quantum systems, such as transformer-based error correction, reinforcement learning for circuit optimization, and AI-driven hardware calibration. Second, it explores quantum-accelerated machine learning algorithms, including variational quantum neural networks, quantum generative adversarial networks, and quantum reinforcement learning. Third, it evaluates industry deployments in sectors such as life sciences, financial services, climate modeling, and pharmaceutical development. Notable examples include IonQ’s quantum chemistry simulations, which achieved 40% efficiency improvements in carbon capture material design; St. Jude’s identification of KRAS protein inhibitors using quantum machine learning with experimental validation; and JP Morgan Chase’s implementation of quantum portfolio optimization. The analysis indicates that the field has advanced from preliminary demonstrations to production-grade applications, especially in drug discovery and molecular simulation, where quantum computing offers measurable benefits. Nevertheless, several challenges remain, including barren plateaus in variational algorithms, scalability constraints in current noisy intermediate-scale quantum (NISQ) devices, requirements for real-time decoding speed, and the necessity for fault-tolerant quantum computing (FTQC) systems. IBM’s 2029 roadmap, which targets 200 logical qubits capable of executing 100 million gates, together with progress in high-rate quantum low-density parity-check codes, outlines a trajectory toward practical fault tolerance. This review makes two principal research contributions: Comprehensive Evaluation Framework for Quantum Advantage Assessment: We establish the first systematic, integrated methodology for determining quantum computing feasibility that combines problem characterization, resource estimation, quantum advantage assessment, and quantum algorithm paradigm selection. This framework consolidates criteria scattered across academic literature and industry practice into a unified decision-making tool applicable across chemistry, optimization, machine learning, and simulation domains. The framework addresses a critical industry need: enabling non-expert practitioners (chemists, financial analysts, materials scientists) to objectively assess quantum computing suitability without requiring deep quantum expertise. Novel Quantum Resource Optimization Algorithms: We present three concrete algorithmic contributions advancing quantum-AI integration: • Data Encoding Efficiency Algorithm that automatically selects optimal qubit encoding strategies (amplitude vs. angle encoding) minimizing total quantum resource consumption • Error Budget Optimization Algorithm that iteratively determines optimal quantum error correction code distance balancing logical error rate targets against physical qubit overhead • Real-time Hardware Specification Aggregation from multiple quantum cloud platforms (IBM Quantum, Amazon Braket, Google, IonQ) enabling dynamic feasibility assessment as hardware capabilities evolve Combined, these contributions establish quantum computing advantage assessment as a rigorous, data-driven discipline rather than ad-hoc expert judgment. The framework enables strategic quantum computing investment decisions, accelerates problem identification for early quantum utility, and provides a reference methodology for standardizing quantum advantage evaluation across academia and industry. This review synthesizes insights from recent academic literature, industry implementations, and expert perspectives to deliver a comprehensive assessment of quantum-AI synergies grounded in this evaluation framework. The mathematical foundations, ranging from quantum Fourier transforms to Gaussian processes, are examined. Best practices for researchers and practitioners are also outlined. The findings suggest that, although universal quantum advantage has not yet been realized, domain-specific quantum-AI applications in chemistry, optimization, and sensing are achieving practical utility in 2024-2025. This development marks a significant transition from theoretical potential to commercial realization, informed by rigorous evaluation methodologies.
Quantum computing is rapidly evolving into an emerging computational infrastructure and is increasingly being used to tackle real-world problems in domains such as chemistry, materials science, logistics, and finance, as well as software engineering problems such as test optimization and project scheduling. Hybrid quantum-classical applications are particularly important because they provide a practical path for integrating quantum capabilities into existing software systems under near-term hardware constraints. However, the engineering of hybrid quantum-classical applications remains largely ad hoc and constrained by hardware limitations including qubit scarcity, noise, and limited connectivity. In this paper, we propose Q-READY to address the lack of systematic methodologies for assessing the feasibility of hybrid solutions prior to costly implementation. Positioned as a Model-Based Systems Engineering (MBSE) approach grounded in Model-Driven Engineering (MDE) principles, Q-READY establishes a structured pipeline encompassing requirements modeling, problem formulation, workflow design, and hardware-aware feasibility assessment, enabling simulation-based evaluation and comparison of candidate solutions under realistic constraints through traceable system-level models and backend-aware abstractions. We illustrate the pipeline with a running credit-portfolio capital-assessment example, showing how requirements, problem structure, strategy choices, workflow behavior, backend assumptions, and feasibility evidence can be linked into a coherent engineering decision. Q-READY is envisioned as an environment that supports executable modeling, constraint evaluation, and predictive analysis. Its expected outcomes include a systematic methodology for hybrid quantum application design, a supporting software platform, benchmark datasets, and empirical design guidelines.