Quantum Echoes for Adaptive Foundation Models: Real-Time Quantum-Assisted Scaling
Murali Krishna Pasupuleti
Abstract: We propose Quantum Echo (QE), a hybrid inference and fine-tuning mechanism that adaptively routes segments of large foundation model workloads to quantum subroutines for latency-critical primitives while preserving prediction quality. QE learns a control policy over quantum depth and echo stabilization to minimize end-to-end latency and energy per token subject to accuracy budgets. Across models ranging from 1B to 70B parameters, synthetic experiments suggest 35–55% latency reductions and 20–40% energy savings, with small accuracy gains from improved calibration. We formalize a cost-risk trade-off, derive regret bounds for the controller, and present a compliance-aware pipeline for secure deployment. Keywords: Quantum machine learning, hybrid inference, foundation models, latency optimization, energy efficiency, governance.
International Journal of Academic and Industrial Research Innovations(IJAIRI)•Oct 30, 2025•0 citations