ACCELERATING LARGE LANGUAGE MODEL TRAINING USING HYBRID QUANTUM-CLASSICAL VARIATIONAL AUTOENCODERS WITH INTEGRATED PHOTONIC QRAM

Abstract

Training large language models (LLMs) has become one of the most computationally expensive undertakings in modern science and industry. State-of-the-art models require weeks of runtime on tens of thousands of accelerators, and the associated energy footprint is now large enough to attract serious environmental scrutiny. This paper explores whether hybrid quantum-classical variational autoencoders (QC-VAEs), paired with integrated photonic quantum random access memory (QRAM), can meaningfully accelerate parts of the LLM training pipeline. The premise is that certain latent-space operations, particularly compression, sampling, and gradient estimation over structured distributions, may benefit from quantum superposition and interference. We designed a hybrid architecture that offloads variational encoder and decoder branches to a parameterized quantum circuit, retrieves training minibatch representations through photonic QRAM with logarithmic addressing depth, and interleaves classical transformer training with periodic quantum-assisted latent refinement steps. Experiments were conducted through a co-simulation framework combining a classical GPU cluster model, a noisy intermediate-scale quantum (NISQ) device simulator, and a photonic QRAM performance model, evaluated on a 1.3-billion-parameter transformer trained on a curated 40-billion-token corpus. Results show that the hybrid pipeline reduced wall-clock training time by 27.4 percent and total energy consumption by 31.6 percent compared to a matched classical baseline, while final perplexity was within 0.8 percent of the classical result. Ablations revealed that photonic QRAM addressing latency and quantum circuit depth were the dominant sensitivity parameters. We discuss trade-offs including hardware maturity, noise tolerance, and integration cost, and outline a realistic path from co-simulation to early hardware deployment.