This technical note presents a human-centered engineering framework for understanding quantum computing in the age of artificial intelligence. It argues that quantum computing should not be treated as magic, hype, or a universal replacement for classical computing, but as a disciplined computational model grounded in probability, linear algebra, measurement, physical systems, and resource constraints. The report connects quantum foundations with artificial intelligence, semiconductor systems, post-quantum security, scientific simulation, engineering education, and civilization-scale technology planning. It proposes a practical suitability framework for deciding when a quantum-AI approach is technically justified, resource-feasible, explainable, and socially useful. This report is a concise scholarly companion to the broader book project Quantum Computing in the Age of Artificial Intelligence: Engineering the Next Civilization by Vikrant Salar, Founder & Principal of SALAR Technologies LLC. It is intended for researchers, engineers, educators, technical founders, policy-minded readers, and reviewers interested in quantum computing, AI-assisted science, hybrid computing systems, post-quantum cryptography, and responsible technology development.
This technical note presents a human-centered engineering framework for understanding quantum computing in the age of artificial intelligence. It argues that quantum computing should not be treated as magic, hype, or a universal replacement for classical computing, but as a disciplined computational model grounded in probability, linear algebra, measurement, physical systems, and resource constraints. The report connects quantum foundations with artificial intelligence, semiconductor systems, post-quantum security, scientific simulation, engineering education, and civilization-scale technology planning. It proposes a practical suitability framework for deciding when a quantum-AI approach is technically justified, resource-feasible, explainable, and socially useful. This report is a concise scholarly companion to the broader book project Quantum Computing in the Age of Artificial Intelligence: Engineering the Next Civilization by Vikrant Salar, Founder & Principal of SALAR Technologies LLC. It is intended for researchers, engineers, educators, technical founders, policy-minded readers, and reviewers interested in quantum computing, AI-assisted science, hybrid computing systems, post-quantum cryptography, and responsible technology development.
The rapid convergence of quantum computing and artificial intelligence is reshaping computational paradigms across cryptography, optimization, sensing, and intelligent systems. This paper surveys and synthesizes recent developments at the intersection of quantum computing and AI, with particular attention to superconducting quantum hardware, quantum reinforcement learning, quantum key distribution optimization, and hybrid AI-driven post-quantum security frameworks. Drawing upon 2026 literature — including advances in high-speed predistortion architectures for flux-bias lines [1], enhanced quantum key distribution parameter optimization [2], quantum reinforcement learning for spectrum allocation in 6G networks [3], hybrid AI–post-quantum encryption for Internet of Medical Things (IoMT) systems [4], and measurement-consistent low-field MRI enhancement via k-space noise modeling [5] — we present a unified perspective on how quantum resources augment classical AI and how AI, in turn, accelerates the practical deployment of quantum technologies. We introduce a conceptual Quantum–AI Convergence Framework that maps hardware constraints, algorithmic opportunities, and security requirements. The analysis highlights persistent challenges in noise resilience, scalability, hybrid orchestration, and regulatory readiness, while outlining promising research directions for trustworthy quantum-enhanced intelligence.
Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.
: The rapid convergence of quantum computing and artificial intelligence is reshaping computational paradigms across cryptography, optimization, sensing, and intelligent systems. This paper surveys and synthesizes recent developments at the intersection of quantum computing and AI, with particular attention to superconducting quantum hardware, quantum reinforcement learning, quantum key distribution optimization, and hybrid AI-driven post-quantum security frameworks. Drawing upon 2026 literature— including advances in high-speed predistortion architectures for flux-bias lines, enhanced quantum key distribution parameter optimization, quantum reinforcement learning for spectrum allocation in 6G networks, hybrid AI–post-quantum encryption for Internet of Medical Things (IoMT) systems, and measurement-consistent low-field MRI enhancement via k-space noise modeling—we present a unified perspective on how quantum resources augment classical AI and how AI, in turn, accelerates the practical deployment of quantum technologies. We introduce a conceptual Quantum–AI Convergence Framework that maps hardware constraints, algorithmic opportunities, and security requirements. The analysis highlights persistent challenges in noise resilience, scalability, hybrid orchestration, and regulatory readiness, while outlining promising research directions for trustworthy quantum-enhanced intelligence.
This paper proposes a novel conceptual framework that integrates the advanced capabilities of quantum computing to address the urgent need for responsible and inclusive Artificial Intelligence (AI) development. It reviews current challenges in AI, such as bias, lack of inclusivity, and the computational limitations faced by classical computing methods in solving complex societal problems. By harnessing quantum computing, this framework aims to overcome these barriers, enabling faster, more efficient AI solutions that are ethically grounded and universally accessible. By adopting a holistic approach that integrates technical innovation with ethical considerations and stakeholder engagement, we believe that quantum computing can serve as a catalyst for the development of AI technologies that are not only more advanced but also more inclusive, responsible, and beneficial for society as a whole. This concept paper serves as a foundational framework for further research, collaboration, and action in the intersection of quantum computing and AI, with the ultimate goal of harnessing the transformative potential of these technologies to address pressing societal challenges and promote human well-being. Keywords: Quantum Computing, AI, Development, Responsible.
Artificial intelligence (AI) infrastructure is becoming increasingly constrained by electricity demand, heat generation, cooling requirements, water consumption, and the lifecycle impacts of specialized hardware. This article proposes a conceptual framework for Quantum AI Engineering for Sustainable Intelligence that treats energy, information, computation, communication, cooling, and water consequences as coupled system-design variables. The framework is informed by a structured narrative synthesis spanning sustainable AI, data-center energy and water use, photonic computing, energy harvesting, quantum sensing, quantum communication, quantum batteries, and hybrid computing. Its central theoretical contribution is the Quantum Energy-Information Interface (QEII): a future systems abstraction that connects observable physical-resource states to computation and communication decisions across classical, photonic, and, where justified, quantum components. The revised framework formalizes cross-layer energy and information flows, introduces state and control variables and a multi-objective sustainability function, separates technologies into deployable, emerging, and exploratory readiness categories, and provides a comparative validation protocol for classical, photonic, quantum-assisted, and hybrid architectures. Water sustainability is treated explicitly through direct cooling water, electricity-related water, embodied water, workload placement, and energy-water trade-offs. The framework does not assume quantum advantage or claim that quantum technologies create energy, eliminate electricity, or enable faster-than-light communication. Instead, it defines testable conditions under which a new component should be considered sustainable: measurable system-level improvement relative to an optimized classical baseline while maintaining accuracy, latency, reliability, security, cost, and lifecycle constraints. The resulting research agenda is intended to support reproducible simulation, prototype development, and eventually system-level evaluation of energy- and water-aware AI infrastructure.
Recent advances in artificial intelligence (AI) and quantum computing are accelerating automation in scientific and engineering processes, fundamentally reshaping research methodologies. This perspective highlights parallels between scientific automation and established Computer-Aided Engineering (CAE) practices, introducing Quantum CAE as a framework that leverages quantum algorithms for simulation, optimization, and machine learning within engineering design. Practical implementations of Quantum CAE are illustrated through case studies for combinatorial optimization problems. Further discussions include advancements toward higher automation levels, highlighting the critical role of specialized AI agents proficient in quantum algorithm design. The integration of quantum computing with AI raises significant questions about the collaborative dynamics among human scientists and engineers, AI systems, and quantum computational resources, underscoring a transformative future for automated discovery and innovation.