The authors designed a modular LLM-driven agent that interprets a user's scientific objective and translates it into an executable SQD workflow.
An LLM agent assembles sample-based quantum chemistry workflows
SQD-Agent converts natural-language requests into hybrid quantum-classical pipelines, with backend integration, profiling and error-mitigation analysis.
Big Tech
Kislaya Tiwari · Anupama Ray
Indian Institute of Technology Delhi · IBM Quantum · IBM Research
Research Digest··2 min read
Tiwari and Ray present SQD-Agent, a framework for constructing quantum chemistry workflows based on Sample-Based Quantum Diagonalization, or SQD, from natural-language instructions.
Why this paper
From IBM Research and 2 others
In one line
SQD-Agent translates natural-language user intent into executable workflows for quantum chemistry using SQD algorithms.
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