Quantum computing is evolving as a revolutionary technology, capable of solving complex problems that are far beyond the reach of traditional computers. Google and China as the global leaders in the race taking their distinct paths with their research and development strategies. Through the Sycamore quantum processor and its Gemini AI, Google focuses on taking the artificial intelligence to the next level, improving optimization, and building practical tools for commercial use. From the Google’s work, especially with the Sycamore quantum processor and its Gemini AI ecosystem, it focuses on advancing artificial intelligence, improving optimization, and building practical tools for commercial use. In contrast, China’s progress, demonstrated through systems like the Jiuzhang photonic platform and the superconducting Zuchongzhi processor, is closely tied to national objectives such as cybersecurity, secure communication, and long-term strategic advantage. This literature review studies the recent researches to compare the two approaches to understand their motivations and examine how their innovations are influencing the future of computing. It also points out existing limitations, challenges in scaling hardware and integrating quantum computing with AI systems. Finally, it emphasizes the key areas where further researches are needed to sustain progress and unleash the full potential of quantum technologies.
Quantum computing is evolving as a revolutionary technology, capable of solving complex problems that are far beyond the reach of traditional computers. Google and China as the global leaders in the race taking their distinct paths with their research and development strategies. Through the Sycamore quantum processor and its Gemini AI, Google focuses on taking the artificial intelligence to the next level, improving optimization, and building practical tools for commercial use. From the Google’s work, especially with the Sycamore quantum processor and its Gemini AI ecosystem, it focuses on advancing artificial intelligence, improving optimization, and building practical tools for commercial use. In contrast, China’s progress, demonstrated through systems like the Jiuzhang photonic platform and the superconducting Zuchongzhi processor, is closely tied to national objectives such as cybersecurity, secure communication, and long-term strategic advantage. This literature review studies the recent researches to compare the two approaches to understand their motivations and examine how their innovations are influencing the future of computing. It also points out existing limitations, challenges in scaling hardware and integrating quantum computing with AI systems. Finally, it emphasizes the key areas where further researches are needed to sustain progress and unleash the full potential of quantum technologies.
The convergence between artificial intelligence (AI) and quantum computing has global economic and security implications, with applications ranging from advancing pharmaceuticals to breaking traditional encryption. In its national and local state plans, China's policymakers clearly seek global leadership in both of these domains, and encourage synergies between them. Traditional research institutions, namely universities and laboratories, are leading theoretical work and basic research and development for quantum computers to power AI applications, quantum machine learning (ML), and other foundational work. Additionally, China's premier cloud service providers are advancing the two technologies with quantum simulators and online platforms for ML and algorithm development. These companies are driving the commercialization of quantum technologies for industries like pharmaceuticals, materials science, and chemistry. China's investments and rapid advancements warrant analytic attention to avoid technological surprise and inform U.S. policymakers and leaders in these fields. To that end, this chapter identifies key entities pursuing research on the nexus between the two technologies, assesses technology paths and obstacles, and provides indicators for future analyses.
Companies like Intel, a pioneer in chip design, are driving the transition from classical computing to the next generation of quantum computing. Alongside Intel, tech giants such as IBM, Microsoft, and Google are also making significant strides in this domain. The race is on to create the world's first functional quantum computer capable of fulfilling the technology's long-promised potential—developing revolutionary materials, encrypting data with near-perfect security, and accurately forecasting climate changes. While such breakthroughs may still be over a decade away, companies like IBM, Microsoft, Google, Intel, and other leading players continue to celebrate even the smallest incremental advancements in this field. Most of these milestones revolve around increasing the number of quantum bits, or qubits—the foundational units of information in a quantum computer—on a processor chip. However, the journey to quantum computing extends far beyond controlling subatomic particles. Quantum computing's expanding capabilities are opening up new possibilities for processing the immense volumes of structured and unstructured data generated today. This progress serves as a powerful enhancement to artificial intelligence (AI), enabling it to evolve toward the next generation of super artificial intelligence (SAI) in the near future. Researchers worldwide have been working since the end of the last century on the development and application of quantum computation.
Two powerful technologies, quantum computing and artificial intelligence (AI), can potentially disrupt sectors and solve some of society’s greatest problems in practically every industry. A study on how quantum computing and AI can work together. We seek to provide a comprehensive literature review encompassing key contributions and problems from both domains. The AI mission’s unique quantum computing strategy and how quantum algorithms can be used to slave for machine learning models and high-speed, sophisticated, accepted handling are explained. Quantum Computing has been shown to improve AI performance, efficiency, turnaround time, and problem-solving capacities. The research continues by considering what this means for quantum computing in AI, where transformational potential may exist, and where AI work currently limits quantum computing’s use. We also explore future research paths, emphasizing the need for interdisciplinary collaboration to properly utilize those technologies and overcome obstacles. This study should be a foundation for future research and discovery at the interface of quantum computing and AI, leading to ground-breaking applications and results.
Quantum computing stands poised to transform numerous fields of modern technology by offering computational capabilities beyond those of classical systems. This survey offers a detailed analysis of major fields, such as artificial intelligence and machine learning (AI/ML), blockchain, cybersecurity, and digital communication, highlighting how they are significantly transformed through advancements in quantum computing. It presents a comparative analysis of current quantum computing paradigms and architectures, and examines major quantum algorithms such as Shor’s integer factorization algorithm, Grover’s search algorithm, and hybrid quantum–classical approaches like QAOA and VQE, highlighting their implications for real-world problem solving. Significant advancements in quantum hardware are surveyed, from increasing qubit counts and improved coherence to progress in error mitigation and emerging quantum processor technologies, and their impact on near-term and long-term computing capabilities is evaluated. Finally, the current limitations of quantum computing are discussed, and forward-looking insights into future research directions are provided, outlining the path toward fully harnessing quantum power across industries.
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 main objective of the research is to analyze the incorporation of quantum computing in artificial intelligence, focusing on applications, tools, and challenges. Through a systematic approach of literature review in academic databases, a growing interest in this integration was detected, particularly in fields such as physics, medicine, chemistry, and cybersecurity. It highlights the effectiveness of quantum algorithms in complex Artificial Intelligence issues such as factorization and unordered search. Challenges include the vulnerability of qubits and the demand for scalable quantum hardware. The research recognizes booming tools for quantum data modeling for Artificial Intelligence, examines innovative applications in materials design and agriculture, and suggests areas for future research. It highlights the importance of creating more efficient quantum algorithms and optimizing their integration.