LLM-Assisted Rewriting Enhances DisCoCat Financial Sentiment Analysis: An Exploratory Evaluation in Quantum NLP

Background and Context

Quantum Natural Language Processing (QNLP) has emerged as a promising interdisciplinary field, yet its practical application in high-stakes domains like finance remains constrained by significant engineering bottlenecks. The Distributional Compositional Categorical (DisCoCat) framework provides a rigorous mathematical foundation for modeling text, mapping linguistic structures directly onto quantum circuits. However, when applied to financial sentiment analysis, DisCoCat exhibits severe sensitivity to parser inputs and struggles with sentence complexity. Financial texts often contain intricate syntactic structures that exceed the capacity of current quantum simulators, leading to prohibitive computational costs and high error rates in circuit generation. These limitations have effectively stalled the deployment of QNLP in real-world financial scenarios, where data is inherently noisy and structurally complex.

To address these challenges, recent research introduces an innovative workflow leveraging Large Language Models (LLMs) for auxiliary preprocessing. This approach does not merely summarize text but employs controlled rewriting strategies to transform moderately complex financial sentences into variants that are both parser-compatible and circuit-efficient. The core objective is to compress, simplify, or decompose sentences while strictly preserving the underlying sentiment semantics. By acting as a bridge between natural language and quantum circuit representation, this method aims to mitigate the parser sensitivity and high simulation costs that currently hinder DisCoCat’s scalability. This exploratory evaluation highlights the potential of using classical AI techniques to overcome the robustness and efficiency gaps in current quantum NLP frameworks.

Deep Analysis

The technical methodology centers on a fine-grained LLM-assisted rewriting pipeline. The system processes financial sentences through three primary operations: compression, which reduces length and structural complexity; simplification, which lowers the depth of grammatical hierarchies; and decomposition, which breaks long sentences into independent clauses for separate quantum mapping. To ensure compatibility with the DisCoCat framework, the workflow incorporates strict filtering configurations that select outputs adhering to specific syntactic requirements and maintaining emotional consistency. The study compares various prompting strategies and model sizes, identifying Prompt B as the optimal configuration for balancing rewrite quality with circuit efficiency. This circuit-aware preprocessing explicitly accounts for downstream quantum circuit constraints, such as qubit count and gate operation numbers, ensuring that the rewritten text translates into a manageable quantum circuit.

Experimental results demonstrate the tangible benefits of this preprocessing approach. The most effective compression variant achieved a reduction of over 70% in average qubit and gate counts compared to the original moderately complex subset. This significant decrease in circuit complexity directly translates to lower simulation costs, making it feasible to process larger volumes of financial data on current quantum hardware. In terms of sentiment analysis accuracy, GPT-4.1-mini utilizing Prompt B achieved an average accuracy of 0.550 ± 0.035, outperforming the baseline method’s 0.521 ± 0.050. These metrics confirm that LLM-assisted rewriting can effectively render previously intractable financial sentences compatible with DisCoCat configurations, thereby enhancing the utility of quantum NLP in financial applications.

Interestingly, the study revealed a counterintuitive finding regarding data scale: increasing the training set size did not improve performance but instead showed a moderate negative correlation (Pearson r = -0.446) with accuracy. This suggests that in the current DisCoCat configuration, larger datasets may introduce noise or complexity that degrades model generalization rather than enhancing it. Ablation experiments further confirmed that prompt design and filtering strategies are more critical to final performance than the inherent capabilities of the LLM itself. This underscores the necessity of precise circuit-aware preprocessing and prompt engineering in building scalable QNLP systems, indicating that quality of transformation outweighs quantity of data in this specific quantum context.

Industry Impact

This research offers a novel perspective on integrating quantum computing into the financial sector by demonstrating how mature LLMs can alleviate the engineering shortcomings of current QNLP frameworks. By effectively addressing parser sensitivity and high computational costs, this workflow paves the way for practical applications of quantum NLP in finance. The emphasis on prompt engineering, filtering mechanisms, and circuit-aware preprocessing provides a replicable framework for the open-source community, fostering academic exchange and technical iteration. It highlights that the path to scalable quantum financial analysis lies not only in hardware advancement but also in sophisticated classical preprocessing layers that adapt data for quantum consumption.

The implications extend beyond immediate accuracy improvements. The study establishes a methodological precedent for using classical AI to compensate for the limitations of quantum algorithms in their nascent stages. This hybrid approach allows financial institutions to experiment with quantum NLP for tasks such as risk assessment and market prediction without requiring fully fault-tolerant quantum computers. By proving that LLM-assisted rewriting can significantly reduce circuit complexity, the research validates the potential for quantum advantage in processing complex financial narratives. This opens avenues for future work in optimizing rewriting algorithms and developing specialized quantum NLP models tailored to financial text, potentially leading to more robust and efficient financial analytics tools.

Outlook

Looking ahead, the integration of LLM-assisted preprocessing with DisCoCat frameworks represents a critical step toward scalable quantum NLP in finance. Future research should focus on refining rewriting algorithms to further enhance semantic preservation while minimizing circuit complexity. Exploring more efficient quantum circuit encoding methods and developing domain-specific models for financial text will be essential for improving accuracy and generalization. Additionally, addressing the negative correlation between training data size and performance requires deeper investigation into noise mitigation and data curation strategies within quantum contexts. As quantum hardware continues to evolve, this hybrid paradigm is poised to play a larger role in financial risk control and market prediction, demonstrating the enduring value of classical AI in enabling quantum technological breakthroughs.

The success of this exploratory evaluation suggests that the immediate future of QNLP lies in collaborative ecosystems where classical and quantum technologies complement each other. By leveraging LLMs for intelligent data preparation, researchers can unlock the potential of quantum circuits for complex linguistic tasks. This approach not only mitigates current hardware limitations but also provides a clear roadmap for integrating quantum computing into mainstream financial analytics. As the field matures, the emphasis will likely shift from mere proof-of-concept demonstrations to optimized, production-ready systems that deliver tangible value in financial decision-making processes. The journey toward quantum advantage in NLP is incremental, and this work marks a significant milestone in that progression.