GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching

TL;DR

GroverGPT is an 8-billion-parameter language model trained on extensive data, capable of approximating quantum search algorithms and outperforming general-purpose models in quantum simulation tasks, revealing insights into classical simulatability limits.

Contribution

This work introduces GroverGPT, a specialized LLM trained on trillions of tokens to simulate quantum search algorithms, demonstrating superior accuracy and generalization over existing models.

Findings

GroverGPT achieves nearly 100% accuracy on small qubit datasets.

The model generalizes well to larger qubit systems, surpassing 95% accuracy.

Accuracy declines with increasing system size, indicating practical simulation limits.

Abstract

Quantum computing is an exciting non-Von Neumann paradigm, offering provable speedups over classical computing for specific problems. However, the practical limits of classical simulatability for quantum circuits remain unclear, especially with current noisy quantum devices. In this work, we explore the potential of leveraging Large Language Models (LLMs) to simulate the output of a quantum Turing machine using Grover's quantum circuits, known to provide quadratic speedups over classical counterparts. To this end, we developed GroverGPT, a specialized model based on LLaMA's 8-billion-parameter architecture, trained on over 15 trillion tokens. Unlike brute-force state-vector simulations, which demand substantial computational resources, GroverGPT employs pattern recognition to approximate quantum search algorithms without explicitly representing quantum states. Analyzing 97K quantum search instances, GroverGPT consistently outperformed OpenAI's GPT-4o (45\% accuracy), achieving nearly 100\% accuracy on 6- and 10-qubit datasets when trained on 4-qubit or larger datasets. It also demonstrated strong generalization, surpassing 95\% accuracy for systems with over 20 qubits when trained on 3- to 6-qubit data. Analysis indicates GroverGPT captures quantum features of Grover's search rather than classical patterns, supported by novel prompting strategies to enhance performance. Although accuracy declines with increasing system size, these findings offer insights into the practical boundaries of classical simulatability. This work suggests task-specific LLMs can surpass general-purpose models like GPT-4o in quantum algorithm learning and serve as powerful tools for advancing quantum research.