Circuit Hypernetworks for Quantum-Augmented Diffusion Language Models
Language models can be adapted by changing the computations applied to individual tokens. Quantum circuits offer one such approach, but evaluating wider circuits inside a large model can be computationally demanding. Here we introduce HyperQ, which adds token-conditioned quantum residual branches to a frozen masked-diffusion language model. A quantum residual branch is a module in each transformer block that reads a token's hidden state, emits the coordinates of that token's circuit, executes it, and adds the measured values back through a residual connection. The backbone remains frozen, and only the added branches are trained. Within each branch, a lightweight circuit hypernetwork emits...
| Authors | Xiaoqiang Wang, Mengyang Xiong, Jun Dai et al. |
|---|---|
| Primary category | quant-ph |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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