Diagram-to-Circuit QNLP for Financial Sentiment Analysis

TL;DR

This paper introduces a hybrid quantum-classical model for financial sentiment analysis that combines chunked quantum circuits with a Transformer encoder to improve interpretability and accuracy.

Contribution

It proposes a novel diagram-to-circuit QNLP model integrating quantum circuits with classical transformers for better semantic modeling in finance texts.

Findings

Sequence model improves macro-F1 score

Chunk attribution highlights key sentence parts

Type embeddings enhance prediction reliability

Abstract

We study a \emph{QDisCoCirc}-inspired, chunked diagram-to-circuit quantum natural language processing (QNLP) model for three-class sentiment classification of financial texts. In our classical simulations, we keep the Hilbert-space dimension manageable by decomposing each sentence into short contiguous chunks. Each chunk is mapped to a shallow quantum circuit, and the resulting Bloch vectors are used as a sequence of quantum tokens. Simple averaging of chunk vectors ignores word order and syntactic roles. We therefore add a small Transformer encoder over the raw Bloch-vector sequence and attach a CCG-based type embedding to each chunk. This hybrid design preserves physically interpretable semantic axes of quantum tokens while allowing the classical side to model word order and long-range dependencies. The sequence model improves test macro-F1 over the averaging baseline and chunk-level attribution further shows that evidential mass concentrates on a small number of chunks, that type embeddings are used more reliably for correctly predicted sentences. For real-world quantum language processing applications in finance, future key challenges include circuit designs that avoid chunking and the design of inter-chunk fusion layers.