Parameter-Shift Rule for Rotation gates

In this tutorial we use the Parameter Shift Rule (PSR) [1, 2] for evaluating the gradients of a variational quantum circuit with respect a variational parameter.

The parameter shift rule in a nutshell

Let's consider a parametrized circuit $\mathcal{U}(\vec{\theta})$, in which we build up an unitary gate of the form:

$\mathcal{G} = \exp \bigl[-i\mu G \bigr]$

which has at most two eigenvalues $\pm r$. Let's consider an observable $B$ and, finally, let $|q_f \rangle$ be the state we obtain by applying $\mathcal{U}$ to $|0\rangle$. We are interested in evaluating the gradients of the following expression:

$f(\mu) \equiv \langle q_f | B | q_f \rangle,$

where we specify that $f$ depends directly on the parameter $\mu$. We are interested in this result because the expectation value of $B$ is typically involved in computing predictions in quantum machine learning problems. The PSR allows us to calculate the derivative of $f(\mu)$ with respect to a evaluating $f$ twice more:

$\partial_\mu = \frac{1}{r} \bigl( f(\mu_+) - f(\mu_-) \bigr),$

where $\mu_\pm$ is obtained as $\mu_\pm = \mu \pm s$ and $s = \pi / 4r$. Finally, if we pick $G$ from the rotations generators we can use $s=\pi/2$ and $r=1/2$.

Loading required features

For building the parameter_shift method we need to use some qibo features in order to implement the previously introduced mathematical components.

    import qibo
    import numpy as np
    from qibo import hamiltonians, gates
    from qibo.models import Circuit
    from qibo.hamiltonians.abstract import AbstractHamiltonian

Now we have can write a parameter_shift function, in which we take into account an hamiltonian (which is our $B$ observable), the index which identify the target variational parameter, the initial state of the circuit and the wigenvalues of the target observable.

    def parameter_shift(
    circuit, hamiltonian, parameter_index, generator_eigenval, initial_state=None
    ):
        # inheriting hamiltonian's backend
        backend = hamiltonian.backend
  
        # defining the shift according to the psr
        s = np.pi / (4 * generator_eigenval)

        # saving original parameters and making a copy
        original = np.asarray(circuit.get_parameters()).copy()
        shifted = original.copy()

        # forward shift and evaluation
        shifted[parameter_index] += s
        circuit.set_parameters(shifted)

        forward = hamiltonian.expectation(backend.execute_circuit(circuit=circuit, initial_state=initial_state).state())

        # backward shift and evaluation
        shifted[parameter_index] -= 2 * s
        circuit.set_parameters(shifted)

        backward = hamiltonian.expectation(backend.execute_circuit(circuit=circuit, initial_state=initial_state).state())

        # restoring the original circuit
        circuit.set_parameters(original)

        return generator_eigenval * (forward - backward)

Now we have a parameter_shift function and we can use it for calculating the gradients of the expected value of $H$ on the final state with respect to $\mu$. In order to check the results, we compare them with the same variables evaluated using the GradientTape() module of tensorflow.

For doing this, we need to load tensorflow and to activate the appropriate qibo's backend.

    # in order to see the difference with tf gradients
    import tensorflow as tf
    qibo.set_backend('tensorflow')

Now we can define the hamiltonian (in this case we use a Pauli Z as observable) and a parametrized circuit.

    # defining an observable
    def hamiltonian(nqubits = 1):
    m0 = (1/nqubits)*hamiltonians.Z(nqubits).matrix
    ham = hamiltonians.Hamiltonian(nqubits, m0)
    return ham

    # defining a dummy circuit
    def circuit(nqubits = 1):
    c = Circuit(nqubits = 1)
    c.add(gates.RY(q = 0, theta = 0))
    c.add(gates.RX(q = 0, theta = 0))
    c.add(gates.M(0))
    return c

This is the moment to write a function which returns the tensorflow values of the gradients.

    # using GradientTape to benchmark
    def gradient_tape(params):
        params = tf.Variable(params)
        
        with tf.GradientTape() as tape:
            c = circuit(nqubits = 1)
            c.set_parameters(params)
            h = hamiltonian()
            expected_value = h.expectation(c.execute().state()) 
        
        grads = tape.gradient(expected_value, [params])
        return grads

In order to check the difference, we randomly generate some parameters and we impose them as variational parameters of the circuit.

    # some parameters
    test_params = np.random.randn(2)
    c.set_parameters(test_params)

Here we are!

Now we can calculate the gradients using the two methods.

    test_hamiltonian = hamiltonian()

    # running the psr with respect to the two parameters
    grad_0 = parameter_shift(circuit = c, hamiltonian = test_hamiltonian, parameter_index = 0, generator_eigenval = 0.5)
    grad_1 = parameter_shift(circuit = c, hamiltonian = test_hamiltonian, parameter_index = 1, generator_eigenval = 0.5)

    tf_grads = gradient_tape(test_params)

    print('Test gradient with respect params[0] with PSR: ', grad_0.numpy())
    print('Test gradient with respect params[0] with tf:  ', tf_grads[0][0].numpy())
    print('Test gradient with respect params[0] with PSR: ', grad_1.numpy())
    print('Test gradient with respect params[0] with tf:  ', tf_grads[0][1].numpy())

And the output should be similar to the following:

    Test gradient with respect params[0] with PSR:  0.09416555057174314
    Test gradient with respect params[0] with tf:   0.09416555057174325
    Test gradient with respect params[0] with PSR:  -0.033018344618441414
    Test gradient with respect params[0] with tf:   -0.033018344618441484

As you can see, the values are identical!

References

[1] Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, Keisuke Fujii, Quantum Circuit Learning, (2018), arXiv:1803.00745v3

[2] Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, Nathan Killoran, Evaluating analytic gradients on quantum hardware, (2018), arXiv:1811.11184v1