| """Independent implementation of arXiv:2503.16678, Fig.2b and Tables 2/3. |
| |
| Inputs are physical (t,x,y), outputs (u,v,p). No reference fields enter forward. |
| The three groups of n circuit angles are independent, as specified by 3*n*L. |
| """ |
| import pennylane as qml |
| import torch |
| from torch import nn |
|
|
|
|
| class QCPINN(nn.Module): |
| def __init__(self, input_dim=3, output_dim=3, hidden_dim=50, |
| n_qubits=5, quantum_layers=1, backend="default.qubit", shots=None): |
| super().__init__() |
| if shots is not None: |
| raise ValueError("The paper requires analytic simulation: shots=None") |
| self.n_qubits = n_qubits |
| self.quantum_layers = quantum_layers |
| self.preprocessor = nn.Sequential(nn.Linear(input_dim, hidden_dim), nn.Tanh(), |
| nn.Linear(hidden_dim, n_qubits)) |
| self.quantum_angles = nn.Parameter(torch.empty(quantum_layers, 3*n_qubits)) |
| self.postprocessor = nn.Sequential(nn.Linear(n_qubits, hidden_dim), nn.Tanh(), |
| nn.Linear(hidden_dim, output_dim)) |
| for block in (self.preprocessor, self.postprocessor): |
| for module in block: |
| if isinstance(module, nn.Linear): |
| nn.init.xavier_normal_(module.weight) |
| nn.init.zeros_(module.bias) |
| nn.init.xavier_normal_(self.quantum_angles) |
| device = qml.device(backend, wires=n_qubits, shots=None) |
|
|
| @qml.qnode(device, interface="torch", diff_method="backprop") |
| def circuit(features, angles): |
| qml.AngleEmbedding(features, wires=range(n_qubits), rotation="X") |
| for layer in range(quantum_layers): |
| for wire in range(n_qubits): |
| qml.RX(angles[layer, wire], wires=wire) |
| for wire in range(n_qubits): |
| qml.RZ(angles[layer, n_qubits+wire], wires=wire) |
| |
| edges = [(n_qubits-1, 0)] + [(i-1, i) for i in range(n_qubits-1, 0, -1)] |
| for index, edge in enumerate(edges): |
| qml.CRX(angles[layer, 2*n_qubits+index], wires=edge) |
| return tuple(qml.expval(qml.PauliZ(w)) for w in range(n_qubits)) |
|
|
| self.circuit = circuit |
|
|
| def forward(self, coordinates): |
| if coordinates.ndim != 2 or coordinates.shape[1] != 3: |
| raise ValueError(f"Expected (batch,3) (t,x,y); got {coordinates.shape}") |
| encoded = self.preprocessor(coordinates) |
| features = torch.stack(self.circuit(encoded, self.quantum_angles), dim=-1) |
| return self.postprocessor(features.to(dtype=coordinates.dtype)) |
|
|
