Quantum phenomena in biology—the role of chunks

Howard F. Gallup

Howard F. Gallup

Howard F. Gallup

C. Reid

Thomas Andrew Waigh

'Some Critical Questions in Biological Physics' discusses eighteen key questions in biological physics, each forming independent chapters that will, by presenting the research in terms of key, unsolved problems, encourage interest in the field. It also provides useful reading for undergraduate physical scientists considering a career in this area.

Sakuma, Takayuki

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.