# Quantum Circuit Design

HyperQ attaches a quantum residual branch in parallel inside every transformer block of a frozen 1.1-billion-parameter masked-diffusion language model (LLaDA-1.1B), where a lightweight circuit hypernetwork continuously emits each token's own IQP circuit coordinates (rotation angles, coupling strengths and measurement axes) from that token's hidden state, executes them on a fixed ring-plus-chord edge set, and adds the measured expectation values back into the query-key-value tensors through a low-rank residual; because the expectation values of this restricted two-body IQP family have an exact closed form whose evaluation cost grows linearly with qubit count, circuits of 16, 32 and 64 qubits can be trained inside that backbone, raising the six-benchmark average from 47.65 to 52.

HyperQ attaches a quantum residual branch in parallel inside every transformer block of a frozen 1.1-billion-parameter masked-diffusion language model (LLaDA-1.1B), where a lightweight circuit hypernetwork continuously emits each token's own IQP circuit coordinates (rotation angles, coupling strengths and measurement axes) from that token's hidden state, executes them on a fixed ring-plus-chord edge set, and adds the measured expectation values back into the query-key-value tensors through a low-rank residual; because the expectation values of this restricted two-body IQP family have an exact closed form whose evaluation cost grows linearly with qubit count, circuits of 16, 32 and 64 qubits can be trained inside that backbone, raising the six-benchmark average from 47.65 to 52.