Paper: arXiv 2511.18804

Authors: Takayuki Sakuma

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.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a novel hybrid approach combining QDisCoCirc quantum natural language processing circuit design with classical Transformer models for financial sentiment analysis. It effectively bridges category-theoretic quantum linguistics with modern sequence modeling, though its empirical methodology relies on classical simulations due to physical qubit constraints.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Financial Sentiment Classification via QNLP"] --> B["Data Input:<br>Financial Texts"]
    B --> C["Chunked Diagram-to-Circuit Mapping:<br>Decompose sentences & map to shallow quantum circuits"]
    C --> D["Extract Quantum Tokens:<br>Generate Bloch vectors for each chunk"]
    D --> E["Hybrid Architecture Process:<br>Add CCG Type Embeddings & Transformer Encoder"]
    E --> F["Outcomes & Findings:<br>1. Transformer improves macro-F1 over averaging<br>2. Evidence concentrates on key chunks<br>3. Type embeddings boost accuracy"]