Does the algorithm work?

PennyLane Help
1

Can I perform classification tasks using The Quantum Graph Recurrent Neural Network? If so, are there any more details, you know I’m a novice

2

Hey @zhong_Feng, welcome to the forum!

From our tutorial (The Quantum Graph Recurrent Neural Network | PennyLane Demos), this is the QGRNN:

def qgrnn(weights, bias, time=None):

    # Prepares the low energy state in the two registers
    qml.StatePrep(np.kron(low_energy_state, low_energy_state), wires=reg1 + reg2)

    # Evolves the first qubit register with the time-evolution circuit to
    # prepare a piece of quantum data
    state_evolve(ham_matrix, reg1, time)

    # Applies the QGRNN layers to the second qubit register
    depth = time / trotter_step  # P = t/Delta
    for _ in range(0, int(depth)):
        qgrnn_layer(weights, bias, reg2, new_ising_graph, trotter_step)

    # Applies the SWAP test between the registers
    swap_test(control, reg1, reg2)

    # Returns the results of the SWAP test
    return qml.expval(qml.PauliZ(control))

If you look at it far enough away, looks like a run-of-the-mill variational quantum circuit! So you can definitely use it for all sorts of applications so long as the inputs, outputs, etc., are interpreted or processed in a way that makes sense for the learning task you’re interested in (classification, in your case).

3

When using QGNN, if my topology graph structure has 39 nodes, do we have to have 39 quantum bits? Can it be run on a simulator? How much larger will the computational cost be?

4

I believe so, yes :slight_smile:. 39 qubits is quite a bit! If you’re going to try to simulate that, I definitely recommend using some of our performance libraries, like PennyLane lightning (Lightning plugins — Lightning 0.35.1 documentation) or Catalyst (Catalyst — Catalyst 0.5.0 documentation).