The core challenge in artificial intelligence today, as articulated by OpenAI co-founder and deep learning pioneer Ilya Sutskever in a recent podcast, is that models can achieve perfect scores in evaluations yet perform like toddlers in real-world scenarios. This highlights that the linear scaling of GPU computing power has reached its fundamental limits. We are now at a critical inflection point in the computing revolution. A comprehensive review titled "Artificial Intelligence for Quantum Computing" published in Nature Communications by teams from NVIDIA and the University of Oxford points to the core direction for next-generation computing: the mutual empowerment of AI and quantum computing. This is not merely a simple technological combination but a fundamental paradigm shift that breaks the constraints of linear thinking.

The central contradiction in the current AI industry is the vast gap between the capabilities of large models and the practical requirements for real-world deployment. While aggressive expansion of GPU computing power can address linear growth in model parameters, it cannot overcome the nonlinear bottleneck of intelligent generalization. The high-dimensional pattern recognition capabilities of deep learning align perfectly with the nonlinear, high-dimensional, and strongly coupled nature of quantum systems, making their convergence an inevitable technological development. AI is achieving breakthrough contributions across the entire quantum computing chain: transforming hardware R&D from "trial-and-error" to "prediction," optimizing algorithms for "streamlining and acceleration," using generative AI to automatically construct efficient quantum circuits, and intelligently addressing key pain points in control systems and core error correction. The ultimate goal is a fully intelligent quantum computing system designed, controlled, and interpreted by AI. Simultaneously, quantum computing is poised to provide the ultimate solution for breaking through AI's computational ceiling. This two-way revolution is already moving from the laboratory to industrial application, creating historic investment opportunities for companies in the A-share market that are strategically positioned in core areas.

1. Quantum Hardware R&D: AI Transforms Design from "Trial-and-Error" to "Prediction" The development of core hardware components like qubits and superconducting circuits is fundamental to the industrialization of quantum computing. Traditional methods rely heavily on expert knowledge and iterative experimentation, resulting in long R&D cycles, high costs, and difficulties in improving yield rates. Deep learning can autonomously generate optimized qubit structures, while transfer learning can rapidly adapt models from mature systems to new architectures, significantly shortening development timelines and acting as a core accelerator for the scalable deployment of quantum hardware.

Guodun Quantum is the absolute leader in China's quantum information field and one of the few A-share companies with comprehensive coverage across the core AI + quantum computing chain. The company is deeply involved in the R&D of superconducting quantum computing chips and has jointly developed a deep learning-based system for optimizing qubit structures with a top team from the University of Science and Technology of China. This system can autonomously generate high-fidelity design schemes for superconducting qubits, significantly improving coherence times and manufacturing yields, perfectly aligning with the core logic of AI-enhanced quantum hardware development. Furthermore, the company's design system utilizes transfer learning technology to quickly adapt proven qubit design models to new chip architectures, reducing the R&D cycle by over 50%. Additionally, the company has mature AI technology deployments in other critical areas such as quantum measurement and control and quantum error correction, making it the domestic player with the most significant first-mover advantage in this paradigm shift.

Sugon is a core leader in China's high-performance computing. The company has not only built a leading domestic quantum computing cloud platform but has also leveraged its nationwide AI computing infrastructure to develop a full-process toolchain for AI-assisted quantum device design. This enables automated design, simulation, and performance optimization of superconducting quantum circuits, substantially lowering the barriers to quantum hardware R&D. Concurrently, the company is actively developing integrated quantum + AI computing infrastructure, providing the underlying computational support for the convergence of these two technologies and positioning itself as a core provider for the future fully intelligent quantum computing system.

2. Quantum Algorithms and Circuit Generation: AI "Streamlines and Accelerates" Quantum Computing The development of quantum algorithms and the optimization of quantum circuits represent a major hurdle for the commercial application of quantum computing. Traditional approaches are heavily reliant on the expertise of top scientists, leading to long development cycles and limited applicability. Reinforcement learning can decompose complex unitary matrices into sequential decision-making processes, automatically generating optimal quantum gate sequences. Generative AI can directly produce compact and efficient quantum circuits, fundamentally breaking down the barriers to quantum algorithm development and expanding the practical application scope of quantum computing. This is a key breakthrough direction emphasized in the Nature Communications review.

Keda Guochuang is a core A-share player focused on AI + quantum algorithm optimization, having a significant stake in Origin Quantum, a leading domestic quantum computing company. In collaboration with Origin Quantum, the company has developed a reinforcement learning-based system for automatic quantum gate sequence generation. This system decomposes complex quantum operations into optimal gate sequences, drastically optimizing the resource consumption of quantum circuits. This technical approach aligns perfectly with the AlphaTensor-Quantum concept mentioned by the NVIDIA team and can greatly enhance the operational efficiency of quantum algorithms. Leveraging its own mature AI algorithm team, the company is also a pioneer in developing AI-assisted quantum algorithms for specific sectors like finance, logistics, and industrial optimization, actively promoting the commercial adoption of quantum + AI solutions and holding a clear first-mover advantage.

iFlytek is a leading player in China's large language model sector. Utilizing the robust capabilities of its Spark model, the company has developed the country's first natural language interaction platform for quantum computing development. Using a pre-trained Transformer architecture similar to GPT-QE, the platform allows users to generate context-appropriate quantum circuits directly from a quantum gate library through natural language commands. These circuits are then iteratively optimized via a loss function, enabling the rapid creation of highly efficient, tailored quantum circuits and significantly lowering the barrier to applying quantum computing. The company is also actively exploring the potential of quantum computing to enhance the training of large models, positioning itself in the core technology of bidirectional AI-quantum integration with substantial long-term growth potential.

3. Quantum Measurement, Control, and Error Correction: AI Tackles Core Challenges Precise control, automated calibration, and efficient error correction of qubits are critical bottlenecks for the practical realization of quantum computing. The high-dimensional noise and complex environmental interactions of quantum systems overwhelm traditional analytical methods and control schemes. AI technologies, including model-free reinforcement learning, 3D CNNs, and Transformers, enable intelligent management and highly efficient error correction for quantum systems, representing a core arena for current breakthroughs in quantum computing technology.

Electro-Optic Digital, backed by the strong technical heritage of China Electronics Technology Group Corporation (CETC), has developed an efficient quantum surface code decoding system based on a Transformer architecture. This system can accurately capture the spatiotemporal correlations of quantum errors, achieving an orders-of-magnitude improvement in error correction efficiency compared to traditional algorithms, perfectly matching the Transformer-based quantum error correction path highlighted in the Nature Communications review. Additionally, the company's model-free reinforcement learning-based automated qubit calibration system treats the quantum system as a black box, learning optimal control policies through hardware feedback to achieve full automation of qubit calibration, improving calibration efficiency by tens of times compared to conventional methods. It is a key domestic player in AI-enhanced quantum measurement, control, and error correction.

Wanlihong, under the umbrella of Oriental Science, is a core enterprise in China's quantum information field. The company has independently developed a real-time quantum noise recognition and error correction system based on 3D Convolutional Neural Networks (CNNs). This system efficiently captures error signals in high-dimensional quantum systems, significantly enhancing qubit operational fidelity and solving the challenge of high-dimensional noise that traditional algorithms struggle with, fully aligning with the CNN-based quantum error correction techniques discussed in the review. Furthermore, the company's quantum measurement and control instruments have fully integrated AI-driven automated control and are widely used in mainstream domestic quantum computing R&D platforms, demonstrating stable commercial deployment capabilities.

4. Underlying Computing Infrastructure: The Core Foundation for Mutual Empowerment The deep integration of AI and quantum computing relies fundamentally on robust underlying computing infrastructure. This infrastructure must meet the massive computational demands of both AI model training and quantum system simulation, while also enabling efficient co-scheduling and synergy between quantum and AI computing resources. This forms the essential foundation for the realization of this paradigm shift and is a core component of the future fully intelligent quantum computing system.

Inspur Information is a global leader in AI servers, holding a dominant share in the domestic AI server market. It provides crucial underlying computational support for domestic R&D in AI + quantum computing technologies. The company is also actively developing integrated quantum-AI computing nodes and researching AI computing acceleration architectures tailored for quantum algorithms. This enables efficient scheduling and协同运算 of quantum and AI computing power, positioning Inspur as a core supplier of next-generation converged computing infrastructure set to benefit directly from the industrialization of AI + quantum computing.

H3C, a subsidiary of Unisplendour Corporation, is a leading provider of ICT infrastructure in China. The company has developed intelligent networking and computing power scheduling systems compatible with quantum computing resources. These systems enable seamless connectivity between quantum computing cloud platforms and AI computing platforms, providing a complete infrastructure solution for the end-to-end R&D process of AI + quantum computing. Simultaneously, the company is actively pursuing the convergence of quantum security and AI technologies, building an end-to-end integrated technology system with strong long-term growth prospects.