Source code for pennylane.fourier.circuit_spectrum

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

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"""Contains a transform that computes the simple frequency spectrum
of a quantum circuit, that is the frequencies without considering
preprocessing in the QNode."""
from typing import Sequence, Callable
from functools import partial
from pennylane import transform
from pennylane.tape import QuantumTape
from .utils import get_spectrum, join_spectra

[docs]@partial(transform, is_informative=True) def circuit_spectrum( tape: QuantumTape, encoding_gates=None, decimals=8 ) -> (Sequence[QuantumTape], Callable): r"""Compute the frequency spectrum of the Fourier representation of simple quantum circuits ignoring classical preprocessing. The circuit must only use simple single-parameter gates of the form :math:`e^{-i x_j G}` as input-encoding gates, which allows the computation of the spectrum by inspecting the gates' generators :math:`G`. The most important example of such gates are Pauli rotations. .. note:: More precisely, the ``circuit_spectrum`` function relies on the gate to define a ``generator``, and will fail if gates marked as inputs do not have this attribute. Gates are marked as input-encoding gates in the quantum function by giving them an ``id``. If two gates have the same ``id``, they are considered to be used to encode the same input :math:`x_j`. The ``encoding_gates`` argument can be used to indicate that only gates with a specific ``id`` should be interpreted as input-encoding gates. Otherwise, all gates with an explicit ``id`` are considered to be input-encoding gates. .. note:: If no input-encoding gates are found, an empty dictionary is returned. Args: tape (QNode or QuantumTape or Callable): a quantum circuit in which input-encoding gates are marked by their ``id`` attribute encoding_gates (list[str]): list of input-encoding gate ``id`` strings for which to compute the frequency spectra decimals (int): number of decimals to which to round frequencies. Returns: qnode (QNode) or quantum function (Callable) or tuple[List[QuantumTape], function]: The transformed circuit as described in :func:`qml.transform <pennylane.transform>`. Executing this circuit will return a dictionary with the input-encoding gate ``id`` as keys and their frequency spectra as values. **Details** A circuit that returns an expectation value which depends on :math:`N` scalar inputs :math:`x_j` can be interpreted as a function :math:`f: \mathbb{R}^N \rightarrow \mathbb{R}`. This function can always be expressed by a Fourier-type sum .. math:: \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* :math:`\Omega_j \subseteq \mathbb{R},` :math:`j=1,\dots,N`. Each spectrum has the property that :math:`0 \in \Omega_j`, and the spectrum is symmetric (for every :math:`\omega \in \Omega_j` we have that :math:`-\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 :math:`x_j, j = 1 \dots N`, only enters into single-parameter gates of the form :math:`e^{-i x_j G}` (where :math:`G` is a Hermitian generator), the frequency spectrum :math:`\Omega_j` is fully determined by the eigenvalues of :math:`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 ``circuit_spectrum`` function computes all frequencies that will potentially appear in the sets :math:`\Omega_1` to :math:`\Omega_N`. **Example** Consider the following example, which uses non-trainable inputs ``x`` and trainable parameters ``w`` as arguments to the qnode. .. code-block:: python import pennylane as qml import numpy as np n_layers = 2 n_qubits = 3 dev = qml.device("default.qubit", wires=n_qubits) @qml.qnode(dev) def circuit(x, w): for l in range(n_layers): for i in range(n_qubits): qml.RX(x[i], wires=i, id="x"+str(i)) qml.Rot(w[l,i,0], w[l,i,1], w[l,i,2], wires=i) qml.RZ(x[0], wires=0, id="x0") return qml.expval(qml.PauliZ(wires=0)) x = np.array([1, 2, 3]) w = np.random.random((n_layers, n_qubits, 3)) res = qml.fourier.circuit_spectrum(circuit)(x, w) >>> print(qml.draw(circuit)(x, w)) 0: ──RX(1.00)──Rot(0.53,0.70,0.90)──RX(1.00)──Rot(0.81,0.38,0.43)──RZ(1.00)─┤ <Z> 1: ──RX(2.00)──Rot(0.56,0.61,0.96)──RX(2.00)──Rot(0.32,0.49,0.77)───────────┤ 2: ──RX(3.00)──Rot(0.11,0.63,0.31)──RX(3.00)──Rot(0.52,0.46,0.83)───────────┤ >>> for inp, freqs in res.items(): >>> print(f"{inp}: {freqs}") 'x0': [-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0] 'x1': [-2.0, -1.0, 0.0, 1.0, 2.0] 'x2': [-2.0, -1.0, 0.0, 1.0, 2.0] .. note:: 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. The input-encoding gates to consider can also be explicitly selected by using the ``encoding_gates`` keyword argument: .. code-block:: python dev = qml.device("default.qubit", wires=1) @qml.qnode(dev) def circuit(x): qml.RX(x[0], wires=0, id="x0") qml.PhaseShift(x[0], wires=0, id="x0") qml.RX(x[1], wires=0, id="x1") return qml.expval(qml.PauliZ(wires=0)) x = np.array([1, 2]) res = qml.fourier.circuit_spectrum(circuit, encoding_gates=["x0"])(x) >>> for inp, freqs in res.items(): >>> print(f"{inp}: {freqs}") 'x0': [-2.0, -1.0, 0.0, 1.0, 2.0] .. note:: The ``circuit_spectrum`` function does not check if the result of the circuit is an expectation, or if gates with the same ``id`` take the same value in a given call of the function. The ``circuit_spectrum`` function works in all interfaces: .. code-block:: python import tensorflow as tf dev = qml.device("default.qubit", wires=1) @qml.qnode(dev, interface='tf') def circuit(x): qml.RX(x[0], wires=0, id="x0") qml.PhaseShift(x[1], wires=0, id="x1") return qml.expval(qml.PauliZ(wires=0)) x = tf.constant([1, 2]) res = qml.fourier.circuit_spectrum(circuit)(x) >>> for inp, freqs in res.items(): >>> print(f"{inp}: {freqs}") 'x0': [-1.0, 0.0, 1.0] 'x1': [-1.0, 0.0, 1.0] """ def processing_fn(tapes): """Process the tapes extract the spectrum of the circuit.""" tape = tapes[0] freqs = {} for op in tape.operations: id = # if the operator has no specific ID, move to the next if id is None: continue # if user has not specified encoding_gate id's, # consider any id is_encoding_gate = encoding_gates is None or id in encoding_gates if is_encoding_gate: if len(op.parameters) != 1: raise ValueError( "Can only consider one-parameter gates as " f"data-encoding gates; got {}." ) spec = get_spectrum(op, decimals=decimals) # if id has been seen before, join this spectrum to another one if id in freqs: spec = join_spectra(freqs[id], spec) freqs[id] = spec # Turn spectra into sorted lists and include negative frequencies for id, spec in freqs.items(): spec = sorted(spec) freqs[id] = [-f for f in spec[:0:-1]] + spec # Add trivial spectrum for requested gate ids that are not in the circuit if encoding_gates is not None: for id in set(encoding_gates).difference(freqs): freqs[id] = [] return freqs return [tape], processing_fn