qnode_spectrum(qnode, encoding_args=None, argnum=None, decimals=8, validation_kwargs=None)[source]

Compute the frequency spectrum of the Fourier representation of quantum circuits, including classical preprocessing.

The circuit must only use gates as input-encoding gates that can be decomposed into single-parameter gates of the form \(e^{-i x_j G}\) , which allows the computation of the spectrum by inspecting the gates’ generators \(G\). The most important example of such single-parameter gates are Pauli rotations.

The argument argnum controls which QNode arguments are considered as encoded inputs and the spectrum is computed only for these arguments. The input-encoding gates are those that are controlled by input-encoding QNode arguments. If no argnum is given, all QNode arguments are considered to be input-encoding arguments.


Arguments of the QNode or parameters within an array-valued QNode argument that do not contribute to the Fourier series of the QNode with any frequency are considered as contributing with a constant term. That is, a parameter that does not control any gate has the spectrum [0].

  • qnode (pennylane.QNode) – QNode to compute the spectrum for

  • encoding_args (dict[str, list[tuple]], set) – Parameter index dictionary; keys are argument names, values are index tuples for that argument or an Ellipsis. If a set, all values are set to Ellipsis. The contained argument and parameter indices indicate the scalar variables for which the spectrum is computed

  • argnum (list[int]) – Numerical indices for arguments with respect to which to compute the spectrum

  • decimals (int) – number of decimals to which to round frequencies.

  • validation_kwargs (dict) – Keyword arguments passed to is_independent() when testing for linearity of classical preprocessing in the QNode.


Function which accepts the same arguments as the QNode. When called, this function will return a dictionary of dictionaries containing the frequency spectra per QNode parameter.

Return type



A circuit that returns an expectation value of a Hermitian observable which depends on \(N\) scalar inputs \(x_j\) can be interpreted as a function \(f: \mathbb{R}^N \rightarrow \mathbb{R}\). This function can always be expressed by a Fourier-type sum

\[\sum \limits_{\omega_1\in \Omega_1} \dots \sum \limits_{\omega_N \in \Omega_N} c_{\omega_1,\dots, \omega_N} e^{-i x_1 \omega_1} \dots e^{-i x_N \omega_N}\]

over the frequency spectra \(\Omega_j \subseteq \mathbb{R},\) \(j=1,\dots,N\). Each spectrum has the property that \(0 \in \Omega_j\), and the spectrum is symmetric (i.e., for every \(\omega \in \Omega_j\) we have that \(-\omega \in\Omega_j\)). If all frequencies are integer-valued, the Fourier sum becomes a Fourier series.

As shown in Vidal and Theis (2019) and Schuld, Sweke and Meyer (2020), if an input \(x_j, j = 1 \dots N\), only enters into single-parameter gates of the form \(e^{-i x_j G}\) (where \(G\) is a Hermitian generator), the frequency spectrum \(\Omega_j\) is fully determined by the eigenvalues of the generators \(G\). In many situations, the spectra are limited to a few frequencies only, which in turn limits the function class that the circuit can express.

The qnode_spectrum function computes all frequencies that will potentially appear in the sets \(\Omega_1\) to \(\Omega_N\).


The qnode_spectrum function also supports preprocessing of the QNode arguments before they are fed into the gates, as long as this processing is linear. In particular, constant prefactors for the encoding arguments are allowed.


Consider the following example, which uses non-trainable inputs x, y and z as well as trainable parameters w as arguments to the QNode.

n_qubits = 3
dev = qml.device("default.qubit", wires=n_qubits)

def circuit(x, y, z, w):
    for i in range(n_qubits):
        qml.RX(0.5*x[i], wires=i)
        qml.Rot(w[0,i,0], w[0,i,1], w[0,i,2], wires=i)
        qml.RY(2.3*y[i], wires=i)
        qml.Rot(w[1,i,0], w[1,i,1], w[1,i,2], wires=i)
        qml.RX(z, wires=i)
    return qml.expval(qml.Z(0))

This circuit looks as follows:

>>> x = np.array([1., 2., 3.])
>>> y = np.array([0.1, 0.3, 0.5])
>>> z = -1.8
>>> w = np.random.random((2, n_qubits, 3))
>>> print(qml.draw(circuit)(x, y, z, w))
0: ──RX(0.50)──Rot(0.09,0.46,0.54)──RY(0.23)──Rot(0.59,0.22,0.05)──RX(-1.80)─┤  <Z>
1: ──RX(1.00)──Rot(0.98,0.61,0.07)──RY(0.69)──Rot(0.62,0.00,0.28)──RX(-1.80)─┤
2: ──RX(1.50)──Rot(0.65,0.07,0.36)──RY(1.15)──Rot(0.74,0.27,0.24)──RX(-1.80)─┤

Applying the qnode_spectrum function to the circuit for the non-trainable parameters, we obtain:

>>> res = qml.fourier.qnode_spectrum(circuit, argnum=[0, 1, 2])(x, y, z, w)
>>> for inp, freqs in res.items():
...     print(f"{inp}: {freqs}")
"x": {(0,): [-0.5, 0.0, 0.5], (1,): [-0.5, 0.0, 0.5], (2,): [-0.5, 0.0, 0.5]}
"y": {(0,): [-2.3, 0.0, 2.3], (1,): [-2.3, 0.0, 2.3], (2,): [-2.3, 0.0, 2.3]}
"z": {(): [-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0]}


While the Fourier spectrum usually does not depend on trainable circuit parameters or the actual values of the inputs, it may still change based on inputs to the QNode that alter the architecture of the circuit.

Above, we selected all input-encoding parameters for the spectrum computation, using the argnum keyword argument. We may also restrict the full analysis to a single QNode argument, again using argnum:

>>> res = qml.fourier.qnode_spectrum(circuit, argnum=[0])(x, y, z, w)
>>> for inp, freqs in res.items():
...     print(f"{inp}: {freqs}")
"x": {(0,): [-0.5, 0.0, 0.5], (1,): [-0.5, 0.0, 0.5], (2,): [-0.5, 0.0, 0.5]}

Selecting arguments by name instead of index is possible via the encoding_args argument:

>>> res = qml.fourier.qnode_spectrum(circuit, encoding_args={"y"})(x, y, z, w)
>>> for inp, freqs in res.items():
...     print(f"{inp}: {freqs}")
"y": {(0,): [-2.3, 0.0, 2.3], (1,): [-2.3, 0.0, 2.3], (2,): [-2.3, 0.0, 2.3]}

Note that for array-valued arguments the spectrum for each element of the array is computed. A more fine-grained control is available by passing index tuples for the respective argument name in encoding_args:

>>> encoding_args = {"y": [(0,),(2,)]}
>>> res = qml.fourier.qnode_spectrum(circuit, encoding_args=encoding_args)(x, y, z, w)
>>> for inp, freqs in res.items():
...     print(f"{inp}: {freqs}")
"y": {(0,): [-2.3, 0.0, 2.3], (2,): [-2.3, 0.0, 2.3]}


The qnode_spectrum function checks whether the classical preprocessing between QNode and gate arguments is linear by computing the Jacobian of the processing and applying is_independent(). This makes it unlikely – but not impossible – that non-linear functions go undetected. The number of additional points at which the Jacobian is computed in the numerical test of is_independent as well as other options for this function can be controlled via validation_kwargs. Furthermore, the QNode arguments not marked in argnum will not be considered in this test and if they resemble encoded inputs, the entire spectrum might be incorrect or the circuit might not even admit one.

The qnode_spectrum function works in all interfaces:

import tensorflow as tf

dev = qml.device("default.qubit", wires=1)

@qml.qnode(dev, interface='tf')
def circuit(x):
    qml.RX(0.4*x[0], wires=0)
    qml.PhaseShift(x[1]*np.pi, wires=0)
    return qml.expval(qml.Z(0))

x = tf.Variable([1., 2.])
res = qml.fourier.qnode_spectrum(circuit)(x)
>>> print(res)
{"x": {(0,): [-0.4, 0.0, 0.4], (1,): [-3.14159, 0.0, 3.14159]}}

Finally, compare qnode_spectrum with circuit_spectrum(), using the following circuit.

dev = qml.device("default.qubit", wires=2)

def circuit(x, y, z):
    qml.RX(0.5*x**2, wires=0, id="x")
    qml.RY(2.3*y, wires=1, id="y0")
    qml.RY(z, wires=0, id="y1")
    return qml.expval(qml.Z(0))

First, note that we assigned id labels to the gates for which we will use circuit_spectrum. This allows us to choose these gates in the computation:

>>> x, y, z = 0.1, 0.2, 0.3
>>> circuit_spec_fn = qml.fourier.circuit_spectrum(circuit, encoding_gates=["x","y0","y1"])
>>> circuit_spec = circuit_spec_fn(x, y, z)
>>> for _id, spec in circuit_spec.items():
...     print(f"{_id}: {spec}")
x: [-1.0, 0, 1.0]
y0: [-1.0, 0, 1.0]
y1: [-1.0, 0, 1.0]

As we can see, the preprocessing in the QNode is not included in the simple spectrum. In contrast, the output of qnode_spectrum is:

>>> adv_spec = qml.fourier.qnode_spectrum(circuit, encoding_args={"y", "z"})
>>> for _id, spec in adv_spec.items():
...     print(f"{_id}: {spec}")
y: {(): [-2.3, 0.0, 2.3]}
z: {(): [-1.0, 0.0, 1.0]}

Note that the values of the output are dictionaries instead of the spectrum lists, that they include the prefactors introduced by classical preprocessing, and that we would not be able to compute the advanced spectrum for x because it is preprocessed non-linearily in the gate qml.RX(0.5*x**2, wires=0, id="x").