Quantum Reservoir-Assisted Context Orchestration for Large Language Models: A 500-Prompt Paired Evaluation

Large language models increasingly operate within systems that provide retrieved knowledge, policies, tool descriptions, workflow state, and other contextual signals. This work investigates Quantum Reservoir Assist, a pre-generation context-orchestration layer that uses a CUDA-Q-based quantum reservoir process to rank candidate contextual signals and construct dynamic decision-support guidance for an otherwise unchanged downstream language model. We evaluate the complete pipeline using a paired benchmark of 500 production-like prompts across ten categories, including architecture and system design, troubleshooting, multi-constraint reasoning, incident response, agent tool planning, security and reliability, and technical explanation. Both experimental paths use the same downstream LLM and generation settings. Across the benchmark, Quantum Reservoir Assist produced a mean absolute increase of approximately 0.172 in the system’s internal answer-readiness/accuracy proxy. The strongest mean proxy improvements occurred in troubleshooting, ambiguous or underspecified requests, conflicting constraints, security and reliability, and multi-constraint tasks. Reservoir assistance also introduced measurable operational overhead, including increased downstream token usage and substantial preprocessing latency under the CUDA-Q simulation configuration used in this experiment. The results demonstrate systematic effects of quantum-reservoir-assisted pre-generation context orchestration, but do not establish ground-truth answer-accuracy improvement or quantum computational advantage. The study therefore motivates controlled ablations against static guidance and classical context-ranking methods.