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arXiv:2604.04089

From Paper to Program: Specification-Guided Implementation of Quantum Many-Body Algorithms with Large Language Model

Yi yuan Zhou

Published Apr 5, 2026• 0 citations
arXivHTMLPDF

Abstract

Turning scientific papers into reliable code requires making implicit computational knowledge explicit. We introduce and evaluate a human-supervised workflow in which large language models develop and review technical specifications before implementing quantum many-body algorithms. The specifications encode conventions, procedures, scaling requirements, and validation gates for mathematical consistency, physical accuracy, and production-scale performance. For the density-matrix renormalization group, all 16 tested specification--implementation model pairings succeed, compared with 6 of 13 direct attempts; two further attempts succeed with a prose specification. For Pfaffian conversion of Hartree--Fock--Bogoliubov states to matrix product states, on eleven matched model--harness configurations, four self-paired workflow attempts pass and no direct attempts do; across all phases, 11 of 26 workflow attempts pass the audit and validation criteria. Cross-model transfer is asymmetric: one implementation model succeeds with all four specifications written by other models, whereas all four reverse-direction attempts fail. A consolidated specification informed by these experiments supports successful implementations in three previously unsuccessful model--harness configurations. Technical specifications therefore guide scalable implementation and help distinguish paper-to-code ambiguity from limitations in implementation capability. We synthesize these findings into the \emph{Paper-to-Program Many-Body} protocol, which turns implementation experience into reusable review criteria, validation gates, and reporting practices. Publicly archived prompts, transcripts, specifications, code, and validation records support independent verification and extension to other many-body algorithms.

DOI
10.48550/arxiv.2604.04089

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Abstract Large language models (LLMs) have demonstrated abilities to perform complex tasks in multiple domains, including mathematical and scientific reasoning. We demonstrate that with carefully designed prompts, LLMs can accurately carry out key calculations in research papers in theoretical physics. We focus on a broadly-used approximation method in quantum physics: the Hartree-Fock method, requiring an analytic multi-step calculation deriving approximate Hamiltonian and corresponding self-consistency equations. To carry out the calculations using LLMs, we design multi-step prompt templates that break down the analytic calculation into standardized steps with placeholders for problem-specific information. We evaluate GPT-4’s performance in executing the calculation for 15 papers from the past decade, demonstrating that, with the correction of intermediate steps, it can correctly derive the final Hartree-Fock Hamiltonian in 13 cases. Aggregating across all research papers, we find an average score of 87.5 (out of 100) on the execution of individual calculation steps. We further use LLMs to mitigate the two primary bottlenecks in this evaluation process: (i) extracting information from papers to fill in templates and (ii) automatic scoring of the calculation steps, demonstrating good results in both cases.
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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.
Quantum computers are moving from research laboratories to industrial machines accessible via the cloud and integrated into high-performance computing facilities. However, translating theoretical quantum protocols into hardware experiments remains a major bottleneck, requiring expertise across protocol design, compilation, simulation, and cloud execution. Here, we introduce an agentic workflow that automates this pipeline on neutral-atom quantum processors (here two Pasqal QPUs available on the cloud) while keeping the researcher in the loop for critical validation. In three case studies from many-body physics and optimization, the agent went from published paper or patent to a QPU campaign run overnight. In particular, human intervention was crucial to ensure scientific validity: the agent selected an inadequate observable in one experiment and constructed a plausible but incorrect hardware diagnosis in another, with both failures detected only through domain-expert review. Finally, we use a second agent to classify a corpus of 633 Rydberg-array arXiv papers and show that nearly half are implementable on present-day QPUs while identifying specific hardware upgrades needed for the rest. Together, these results demonstrate that agentic workflows provide a practical bridge between theoretical ideas and physical hardware, opening quantum experimentation to a much broader scientific community.