What question did this study set out to answer?

This research aims to evaluate how quantum computing can enhance artificial intelligence through various approaches.

July 11, 20260 citationsOpen Access

Research on the application potential and synergistic effects of quantum computing in the field of artificial intelligence: Integrative analysis centered on quantum machine learning (VQC/QNN)

Key Points

This research aims to evaluate how quantum computing can enhance artificial intelligence through various approaches.
Systematic analysis of quantum computing principles relevant to AI.
Comparison of Variational Quantum Circuits and Quantum Neural Networks in the NISQ era.
Examination of application areas including finance, pharmaceuticals, and natural language processing.
Identified quantum computing as a potential co-processor for classical AI tasks.
Outlined key challenges such as input/output bottlenecks and noise mitigation in quantum systems.
Emphasized the importance of developing fault-tolerant quantum computing for long-term AI advancements.

Abstract

The rapid development of deep learning and large language models (LMs) has led to a "computing inflation" phenomenon in the demand for computing power, energy consumption, and training time for artificial intelligence (AI). As classical computing gradually approaches its scalability bottleneck due to the slowing of Moore's Law and limitations imposed by memory walls and energy consumption, quantum computing (QC), with its properties such as superposition, entanglement, and quantum interference, is considered a candidate computing paradigm that may provide acceleration for specific sub-problems and bring new inductive biases to representation learning. This paper focuses on the application potential of quantum computing in artificial intelligence. It first establishes the theoretical foundation of quantum computing, then systematically outlines collaborative pathways that quantum computing can explore from three perspectives: AI training acceleration, data processing and representation learning, and neural networks and combinatorial optimization. Furthermore, this paper focuses on the most feasible quantum machine learning (QML) architectures for the Noisy Intermediate-Scale Quantum (NISQ) era, deeply comparing the design philosophy, data encoding strategies, parameter shift rule, shot complexity, and barren plateaus of Variational Quantum Circuits (VQC) and Quantum Neural Networks (QNN). Finally, this paper analyzes potential application areas in finance, pharmaceuticals/materials, natural language processing, and recommendation systems, and discusses key obstacles such as input/output bottlenecks, noise and error mitigation, benchmarking and verifiability, and governance and security. The paper concludes that in the short to medium term, quantum computing is more likely to serve as a "co-processor" for classical AI, providing gains in specific subtasks through a hybrid quantum-classical workflow. In the long term, however, the development of fault-tolerant quantum computing and scalable quantum error correction is necessary to achieve reproducible and cost-effective quantum advantages across a wider range of AI tasks.

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