INTEGRATION_GUIDE.md

Integration Guide: Quantum Attention Head

1. Prerequisites

• torch $\ge 2.0$
• numpy $\ge 1.21$
• cupy (Optional, for GPU acceleration)

2. Implementation Steps

Step 1: QPU Initialization

Initialize the HyperspaceQPU with the desired qubit count and bond dimension.

from core.mini_qpu import HyperspaceQPU
qpu = HyperspaceQPU(n_qubits=4, bond_dimension=16)

Step 2: Projection Layer

Create a linear layer to map the model dimension $d_{\text{model}}$ to the QPU parameter space.

self.proj = nn.Linear(d_model, n_qubits * 2)

Step 3: The Forward Pass

Integrate the QPU call within the attention mechanism:

1. Project input to rotation angles.
2. Apply gates to the QPU state.
3. Extract the expectation value as a scalar.
4. Scale the attention output by this scalar.

Step 4: Block Integration

Wrap the QuantumAttentionHead within a standard Transformer block, ensuring a LayerNorm follows the quantum path to prevent gradient explosion.

3. Verification

Run the sanity test provided in core/sovereign_transformer.py. A successful integration is confirmed when the output shape matches the input shape and the expectation value is non-zero.