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.
Quantum artificial intelligence (QAI) is a topic of study that blends quantum computing and AI. In order to investigate the possibilities of fruitful cooperation across both disciplines of computer science (CS), the authors will attempt to convey some of the relevant properties of both artificial intelligence (AI) and quantum computing (QC) from an academic point of view. The key artificial intelligence applications that can be addressed more effectively by employing the current generation of quantum computers will be covered in this chapter, along with a review of the fundamental components of quantum computing. There are several similarities between quantum computing and artificial intelligence. Quantum computing can increase the training speed and processing capability of artificial intelligence and machine learning algorithms at a lower cost. This chapter discusses the fundamental ideas that enable scientists and engineers to combine AI with quantum computing.
经典计算正逼近物理极限，而含噪声的中等规模量子硬件面临校准与控制瓶颈，这催化了量子人工智能这一前沿交叉领域的诞生。量子人工智能旨在利用人工智能与量子计算的协同作用，解决彼此的瓶颈问题。本综述系统性地分析量子人工智能的双向赋能框架。在“AI for Quantum”方面，AI正成为克服中等规模量子硬件瓶颈的工具，关键应用包括设备自动化校准与高保真读出、开发超越传统算法的AI量子纠错解码器及AI驱动的量子线路优化。在“Quantum for AI”方面，量子计算正为 AI 算法提供新的计算范式与模型设计思路，从早期的HHL、QSVM算法演进到中等规模量子时代主流的变分量子算法和量子神经网络，同时综述了该领域面临的贫瘠高原等可训练性挑战及其缓解策略，并涵盖了量子优化及量子原生智能模型。总结指出，量子人工智能的双向融合有望成为推动量子计算从中等规模量子时代迈向容错计算、构建下一代混合量子-经典智能系统的重要路径之一。
The development of quantum artificial intelligence (QAI) may lead a new computing revolution. This paper studies a model of quantum inference engine (QIE), which is a novel architecture designed to enhance quantum artificial intelligence by leveraging quantum principles. It discusses a role of the quantum superposition and entanglement in the transition from classical to quantum computational models, which surpasses the classical inference engines. The details of QIE’s structure is provided, from the quantum knowledge base to the inference mechanisms, demonstrating the capacity in the parallel processing and complex probabilistic reasoning. This research outlines the significant advancements in computational inference with the quantum technologies, especially in the era of the Noisy Intermediate-Scale Quantum (NISQ). The QIE shows its improved efficiency, scalability, and accuracy in handling intricate data and probabilistic models. The quantum inference engine will be useful for the research and applications in quantum artificial intelligence.