• Subjects Mathematics and computing Physics Abstract Understanding the high-level conceptual structure of quantum algorithms from their low-level circuit representations is a critical task for verification, debugging, and education. • While traditional numerical simulators can calculate output probabilities, they do not explicitly surface the underlying algorithmic logic, such as the function of an oracle or embedded symmetries. • In this work, we shift the focus from numerical simulation tosymbolic analysis, investigating whether large language models (LLMs) can automaticallyinterpretquantum circuits and articulate their logic in a human-readable format. • We introduce GroverGPT+, a model that leverages Chain-of-Thought reasoning and quantum-native tokenization to analyze Grover’s search algorithm. • We use Grover’s algorithm as a controlled testbed, as its well-defined analytical properties allow for rigorous verification of the model’s reasoning process. • Our primary finding is that GroverGPT+ successfully identifies the oracle and its marked states directly from circuit representations.
Article Summaries:
- Researchers have developed GroverGPT+, a large‑language‑model (LLM) that performs symbolic analysis of quantum circuits, using Chain‑of‑Thought reasoning and a quantum‑native tokenization scheme. Tested on Grover’s search algorithm, the model automatically identifies the oracle and its marked states directly from the circuit representation, producing a structured reasoning trace rather than just output probabilities. The study introduces a benchmark for this symbolic‑analysis task and evaluates how performance scales with increasing qubit counts. Findings suggest LLMs can serve as automated tools for quantum‑algorithm verification, debugging, and education, and hint at a new way to assess algorithmic complexity through classical learnability. The code and data are publicly available on GitHub.
Sources: