Quantum Machine Learning for Accelerating Large Model Training

Abstract

Aiming at the practical bottlenecks in current large model training, such as high computing cost, low efficiency, exponential growth of long-sequence computing complexity, and increasing pressure of hardware energy consumption and carbon emissions, based on the hardware conditions and technical boundaries in the era of Noisy Intermediate-Scale Quantum (NISQ) computing, this paper proposes an original Cascaded Quantum-Assisted Computing Paradigm, and systematically explores the acceleration path and implementation mechanism of the deep integration of quantum machine learning technology and classical large model training process. By embedding the two core technologies independently proposed, Quantum Amplitude Attention Pooling and Quantum Random Circuit Embedding, into the core modules of Transformer architecture with the highest computing load and most prominent complexity, this study constructs a quantum-classical hybrid training framework with stable structure, strong compatibility and direct deployability on existing quantum cloud platforms. On the premise of not destroying the overall structure and training logic of classical models, it realizes dynamic offloading and collaborative computing of high-complexity subtasks to quantum processors. Experimental results show that on the current mainstream NISQ quantum computing devices, the proposed framework can achieve a stable improvement of 18%—25% in overall training efficiency in public text classification standard tasks, while the model accuracy loss is controlled within 0.5 percentage points, with strong engineering application value. This paper further systematically analyzes the practical constraints such as quantum hardware noise, quantum-classical data interaction overhead, and algorithm-hardware adaptability. Combined with the future evolution trend of fault-tolerant quantum computing, it comprehensively prospects the long-term development path, technological breakthrough direction and industrial application scenarios of quantum machine learning in the field of large model training, providing new theoretical ideas and practical references for breaking through the computing power ceiling of large model training under the classical computing system.