High dimensionality in semantic representations presents challenges for near-term quantum machine learning (QML). Quantum circuits have limits concerning the direct processing of many input features. A hybrid quantum, classical pipeline converts high-dimensional sentence embeddings into more manageable formats suitable for variational quantum classification. The workflow combines a pretrained sentence-embedding model with dimensionality reduction, angle encoding, a variational quantum circuit (VQC), and a concluding classical decision layer. Principal component analysis (PCA) and alternative techniques are systematically examined to achieve strong dimensionality reduction.

Supervised dimension reduction boosts performance of quantum natural language processing

A linear discriminant analysis employing only five dimensions achieved an accuracy rate of 85.3%, exceeding the capabilities of conventional methods needing 384 dimensions and marking a significant step forward in near-term quantum machine learning applications. Previously, compressing sentence embeddings without substantial information loss proved impossible due to limitations in handling numerous input features within existing quantum circuits. Supervised dimensionality reduction techniques preserve task-relevant data far more effectively compared to variance-based compression strategies such as principal component analysis.

Neighborhood Components Analysis attained an accuracy of 83.1 percent with five dimensions, closely approaching the baseline result of 85.1 percent obtained using the complete original dimensionality of 384 features. Further investigation revealed that principal component analysis retained approximately 16.4 percent variance when reducing embeddings down to eight dimensions; this yielded a classification accuracy of only 63.4 percent, a considerable decline relative to supervised methods. This breakthrough enables compact representations suitable for hybrid quantum-classical natural language processing models and opens avenues previously inaccessible due to computational constraints.

The workflow integrates a pretrained sentence-embedding model alongside dimensionality reduction, angle encoding, a variational quantum circuit, and a classical decision layer. PCA, NCA, and LDA were systematically compared to assess both unsupervised and supervised approaches to representation learning. Experiments utilising the TREC question-classification dataset examined how changes in representation size affect information retention, qubit count, and overall classification performance.

Reducing 768-dimensional embeddings to 3, 4, 5 or 8 dimensions using PCA retained approximately 8.2%, 10.2%, 11.9% and 16.4% of variance respectively. Preliminary accuracies reached only 50.3%, 51.2%, 57.9% and 63.4%. Compressing sentence embeddings offers a potential solution to a key bottleneck hindering progress within quantum natural language processing because current quantum circuits lack the capacity for directly processing data from modern linguistic models.

However, this reliance on classical pre-processing introduces its own complexities as highlighted by ongoing work with DisCoCat and DisCoCirc frameworks which attempt compositional modelling; these approaches demand significant upfront linguistic analysis while rapidly increasing computational demands alongside longer sentences. Acknowledging concerns about the computational cost associated with linguistic pre-processing using frameworks like DisCoCat and DisCoCirc remains important.

Reaching up to 85.3% accuracy utilising five dimensions demonstrates a pathway towards employing limited qubit counts in near-term quantum circuits, overcoming inherent limitations in input feature capacity. The findings establish that supervised techniques for reducing data complexity are vital when integrating classical analysis with quantum computing; preserving overall variance alone, as done by principal component analysis, is insufficient for maintaining task performance. This success moves beyond merely demonstrating feasibility, highlighting how careful selection of dimensionality reduction methods becomes an architectural necessity rather than simple preprocessing.

The research demonstrated that high-dimensional sentence embeddings can be compressed into representations using only five dimensions without substantial loss of classification accuracy on the TREC question-classification dataset. Employing linear discriminant analysis and neighbourhood components analysis achieved accuracies of 85.3% and 83.1%, respectively, indicating supervised dimensionality reduction preserves information more effectively than unsupervised approaches like principal component analysis which retained limited variance with lower results.

These findings are important because they suggest a method for utilising near-term quantum circuits, limited in their ability to process many inputs, with complex linguistic data. The authors showed this approach moves beyond proof-of-concept by establishing how careful selection of these methods is essential when combining classical processing with quantum computation.

👉 More information
🗞 Hybrid Quantum-Classical NLP Classification with Compact Semantic Representations: An Experimental Analysis of Representation Compression
✍️ Ali Hassan, Zijia Zhao and Maha A. Metawei
🧠 ArXiv: https://arxiv.org/abs/2609.10089

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