Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification
We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed. Our architecture, Quantum-Embedded Attention (QEA), uses a learnable projector to compress backbone features into an $n_q$-dimensional angle vector, a shallow PQC to map those angles to one- and two-qubit Pauli expectations, and a classical attention decoder to produce class logits. We hypothesized the PQC would improve accuracy or seed-to-seed stability over a classical map with matched input/output dimensions. We test this with an interface-matched $2\times2$ ...
| Authors | Hao-Yuan Chen |
|---|---|
| Primary category | quant-ph |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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