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.
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.
I evaluated whether a quantum AI reservoir can improve the decisions produced by retrieval-augmented generation (RAG) and tool-using Agentic workflows. Two complementary experiments used the same five workflow variants: RAG, RAG + QR, Agentic, Agentic + QR, and LLM + QR. Each experiment contained 201 main tasks and three repeats, yielding 6,030 answers overall. The first experiment used synthetic planning scenarios; the second used numerical quantities extracted from quantum-domain books and technical papers. In the designated first repeat, QR increased complete structured success from 75.1% to 90.5% for synthetic RAG and from 27.4% to 52.2% for synthetic Agentic. On real-source tasks, the corresponding rates increased from 46.8% to 57.7% and from 34.3% to 55.7%. Both paired improvements were positive in every repeat of both experiments. LLM + QR achieved 94.0% on synthetic tasks and 54.7% on real-source tasks in the first repeat, exceeding both unassisted baselines in each experiment. The results support the integrated reservoir-guidance module as a useful addition to the evaluated decision workflows and motivate a simpler single-call architecture when all required evidence is available. The study evaluates a simulator-backed quantum/classical module; it does not establish quantum computational advantage or isolate the quantum component from its classical guidance components.
External Quantum Reservoir Assist is designed to help an AI assistant identify the core of a difficult request and organize a useful response by adding dynamically generated guidance through a REST or MCP integration. This study evaluates final answers from an archived experiment using one response pair from each of 50 synthetic scenario families. Thirty-nine pairs formed the primary complex decision-support group, while eleven provided routine, constraint-challenge, and technical-explanation controls. Within the primary group, reservoir-assisted answers were preferred in 17 cases, baseline answers in 8, with 13 ties and 1 unclear case. The tie-adjusted preference rate was 61.8% (95% family-bootstrap interval: 48.7–73.7%). The clearest descriptive gains were observed in actionability and validation guidance, while major-error flags occurred equally often in both conditions. Assistance increased total token usage by 32.9% and median paired estimated end-to-end time by 7.51 seconds. The results provide directional evidence that external reservoir guidance can improve practical decision support for selected complex AI-assistant tasks, while also demonstrating substantial uncertainty and operational overhead. The study does not establish general response superiority, reduced factual-error rates, or a quantum-specific advantage. The accompanying minimized dataset and executable analysis reproduce the reported statistics and figures without additional model calls.
The integration of large language models (LLMs) into scientific research is accelerating the realization of autonomous ``AI Scientists.'' While recent advancements have empowered AI to formulate hypotheses and design experiments, a critical gap remains in the execution of these tasks, particularly in the domain of quantum computing (QC). Executing quantum algorithms requires not only generating code but also managing complex computational resources such as QPUs and high-performance computing (HPC) clusters. In this paper, we propose an AI-driven framework specifically designed to bridge this execution gap through the implementation of a Model Context Protocol (MCP) server. Our system enables an LLM agent to process natural language prompts submitted as part of a job, autonomously executing quantum computing workflows by invoking our tools via the MCP. We demonstrate the framework's capability by performing essential quantum algorithmic primitives, including sampling and computation of expectation values. Key technical contributions include the development of an MCP server for quantum execution, a pipeline for interpreting OpenQASM code, an automated workflow with CUDA-Q for the ABCI-Q hybrid platform, and an asynchronous execution pipeline for remote quantum hardware using the Quantinuum emulator via CUDA-Q. This work validates that AI agents can effectively abstract the complexities of hardware interaction through an MCP-based architecture, thereby facilitating the automation of practical quantum research.
This study evaluates an external simulator-backed quantum/classical guidance component with optional graph context across direct LLM advice, retrieval-augmented generation (RAG), and tool-using Agentic workflows. Three conditions are compared: Baseline, QR only, and QR + graph. The RAG and Agentic evaluation comprises 17 tasks, three conditions, and three repeats per workflow, producing 306 answers. A deterministic evaluator measures complete success through structured decision correctness, quotation validity, decisive-source coverage, and, for Agentic workflows, consistency with a simulated commit. Complete RAG success was 88.2% for Baseline, 92.2% for QR only, and 98.0% for QR + graph. Complete Agentic success was 62.7%, 54.9%, and 86.3%, respectively. Relative to QR only, graph context increased success by 5.9 percentage points in RAG and 31.4 points in Agentic. The combined configuration exceeded Agentic Baseline in all three repeats. Task-conditional bootstrap intervals support a clearer benefit in Agentic; the RAG intervals include zero. A separate single-request advisory comparison produced weighted LLM-review scores of 87.5, 95.0, and 95.0 out of 100. These exploratory scores are reported separately and are not accuracy percentages. The release includes the article and two public JSON supplements containing anonymized trial outcomes, summaries, evaluation settings, and advisory score vectors. Proprietary implementation, prompts, source passages, and model response texts are withheld. The findings concern the integrated guidance and evidence-acquisition system on the evaluated tasks. They do not establish quantum computational advantage, isolate the quantum component as the cause of improvement, or demonstrate general superiority. The main evaluation is a follow-up run on previously inspected tasks, not an unseen holdout.
We demonstrate that large language model (LLM) agents can autonomously perform tensor network simulations of quantum many-body systems, achieving approximately 90% success rate across representative benchmark tasks. Tensor network methods are powerful tools for quantum simulation, but their effective use requires expertise typically acquired through years of graduate training. By combining in-context learning with curated documentation and multi-agent decomposition, we create autonomous AI agents that can be trained in specialized computational domains within minutes. We benchmark three configurations (baseline, single-agent with in-context learning, and multi-agent with in-context learning) on problems spanning quantum phase transitions, open quantum system dynamics, and photochemical reactions. Systematic evaluation using DeepSeek-V3.2, Gemini 2.5 Pro, and Claude Opus 4.5 demonstrates that both in-context learning and multi-agent architecture are essential. Analysis of failure modes reveals characteristic patterns across models, with the multi-agent configuration substantially reducing implementation errors and hallucinations compared to simpler architectures.
This study evaluates a protocol-guided large language model (LLM) agent workflow for quantum program synthesis and execution. A versioned YAML protocol specifies interface and quantum-semantic requirements while leaving circuit design to the model. The workflow combines Qiskit circuit generation, evaluator-guided repair, and quantum processing unit (QPU) deployment. A matched ablation evaluates four LLM endpoints using the variational quantum eigensolver (VQE) for H2, the quantum approximate optimization algorithm (QAOA) for MaxCut, and Fashion-MNIST quantum-kernel classification. Across 240 trials, protocol guidance increased evaluator completion from 80.0\% to 99.2\%, reduced mean repairs from 2.56 to 0.75, and reduced mean agent time from 55.70 to 30.58~s. Provider-reported token use decreased for three of four endpoints. Completed VQE trials meeting chemical accuracy increased from 16/40 to 29/40, while valid QAOA approximation ratios remained comparable. Semantic review identified rank-one fidelity kernels, reinforcing the distinction between evaluator completion and semantic validity. A 210-run IBM QPU study evaluated the combined generation-and-selection pipelines and confirmed deployment feasibility. The results support protocol guidance as a software-reliability layer that must be complemented by quantum-semantic validation.