Optimizing a circuit that returns `qml.state()`

PennyLane Help
1

I am trying to optimize a circuit using this loss function \left\langle {{\Psi}\left( {{{\mathbf{\theta }}}} \right)\left| {\hat H} \right|{\Psi}\left( {{{\mathbf{\theta }}}} \right)} \right\rangle + \beta \left| {\left\langle {{\Psi}\left( {{{\mathbf{\theta }}}} \right)\left| {{\Psi}_0} \right.} \right\rangle } \right|^2

I think it would make sense if the circuit return qml.state() rather than qml.expval(H) because I have to use state to calculate the 2nd term. However, Pennylane seems not supporting it. I receive

ValueError: Computing the gradient of circuits that return the state with the parameter-shift rule gradient transform is not supported, as it is a hardware-compatible method.

Is there a way to circumvent this problem :slight_smile:

EDIT: Here I am using lightning.qubit, but default.qubit also didn’t work

Below is my code

import pennylane as qml
from pennylane import numpy as np
import optax
import jax

dev = qml.device("lightning.qubit", wires=qubits)

h2_dataset = qml.data.load("qchem", molname="H2", bondlength=0.742, basis="STO-3G")
h2 = h2_dataset[0]
H, qubits = h2.hamiltonian, len(h2.hamiltonian.wires)

@qml.qnode(dev)
def circuit(param):
    qml.BasisState(h2.hf_state, wires=range(qubits))
    qml.DoubleExcitation(param, wires=[0, 1, 2, 3])
    return qml.state()

def loss_fn_1(theta):
    """
    Pure expectation value
    """
    state = circuit(theta)
    return state.conj().T @ hamiltonian_to_matrix(h2.hamiltonian, h2.hamiltonian.wires) @ state

theta = np.array(0.)

# store the values of the cost function
energy = [loss_fn_1(theta)]
conv_tol = 1e-6
max_iterations = 100
opt = optax.sgd(learning_rate=0.4)

# store the values of the circuit parameter
angle = [theta]

opt_state = opt.init(theta)

for n in range(max_iterations):
    gradient = jax.grad(loss_fn_1)(theta)
    updates, opt_state = opt.update(gradient, opt_state)
    theta = optax.apply_updates(theta, updates)
    
    angle.append(theta)
    energy.append(loss_fn_1(theta))

    conv = np.abs(energy[-1] - energy[-2])

    if n % 2 == 0:
        print(f"Step = {n},  Energy = {energy[-1]:.8f} Ha")

    if conv <= conv_tol:
        break
2

Hi @mchau,

What device are you using?
I think you forgot to copy-paste this line of your code.

1 Like
3

Hi @CatalinaAlbornoz

I used both lightning.qubit and "default.qubit. Updated the question :slight_smile:

4

Hi @mchau,

I noticed you also forgot to include the hamiltonian_to_matrix function. I modified the return of your loss_fn_1 function in order to replicate your issue.

If you use the backprop differentiation method with default.qubit you should no longer see the same error.

dev = qml.device("default.qubit", wires=qubits)

@qml.qnode(dev, diff_method="backprop")

Let me know if this solves your issue!

1 Like