# qml.templates.subroutines.QuantumPhaseEstimation¶

class QuantumPhaseEstimation(unitary, target_wires, estimation_wires, do_queue=True)[source]

Performs the quantum phase estimation circuit.

Given a unitary matrix $$U$$, this template applies the circuit for quantum phase estimation. The unitary is applied to the qubits specified by target_wires and $$n$$ qubits are used for phase estimation as specified by estimation_wires.

This circuit can be used to perform the standard quantum phase estimation algorithm, consisting of the following steps:

1. Prepare target_wires in a given state. If target_wires are prepared in an eigenstate of $$U$$ that has corresponding eigenvalue $$e^{2 \pi i \theta}$$ with phase $$\theta \in [0, 1)$$, this algorithm will measure $$\theta$$. Other input states can be prepared more generally.

2. Apply the QuantumPhaseEstimation circuit.

3. Measure estimation_wires using probs(), giving a probability distribution over measurement outcomes in the computational basis.

4. Find the index of the largest value in the probability distribution and divide that number by $$2^{n}$$. This number will be an estimate of $$\theta$$ with an error that decreases exponentially with the number of qubits $$n$$.

Note that if $$\theta \in (-1, 0]$$, we can estimate the phase by again finding the index $$i$$ found in step 4 and calculating $$\theta \approx \frac{1 - i}{2^{n}}$$. The usage details below give an example of this case.

Parameters
• unitary (array) – the phase estimation unitary, specified as a matrix

• target_wires (Union[Wires, Sequence[int], or int]) – the target wires to apply the unitary

• estimation_wires (Union[Wires, Sequence[int], or int]) – the wires to be used for phase estimation

Raises

QuantumFunctionError – if the target_wires and estimation_wires share a common element

Consider the matrix corresponding to a rotation from an RX gate:

import pennylane as qml
from pennylane.templates import QuantumPhaseEstimation
from pennylane import numpy as np

phase = 5
target_wires = [0]
unitary = qml.RX(phase, wires=0).matrix


The phase parameter can be estimated using QuantumPhaseEstimation. An example is shown below using a register of five phase-estimation qubits:

n_estimation_wires = 5
estimation_wires = range(1, n_estimation_wires + 1)

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

@qml.qnode(dev)
def circuit():
# Start in the |+> eigenstate of the unitary

QuantumPhaseEstimation(
unitary,
target_wires=target_wires,
estimation_wires=estimation_wires,
)

return qml.probs(estimation_wires)

phase_estimated = np.argmax(circuit()) / 2 ** n_estimation_wires

# Need to rescale phase due to convention of RX gate
phase_estimated = 4 * np.pi * (1 - phase_estimated)

 base_name Get base name of the operator. eigvals Eigenvalues of an instantiated operator. generator Generator of the operation. grad_method Gradient computation method. grad_recipe Gradient recipe for the parameter-shift method. inverse Boolean determining if the inverse of the operation was requested. matrix Matrix representation of an instantiated operator in the computational basis. name Get and set the name of the operator. num_params num_wires par_domain parameters Current parameter values. string_for_inverse wires Wires of this operator.
base_name

Get base name of the operator.

eigvals
generator

Generator of the operation.

A length-2 list [generator, scaling_factor], where

• generator is an existing PennyLane operation class or $$2\times 2$$ Hermitian array that acts as the generator of the current operation

• scaling_factor represents a scaling factor applied to the generator operation

For example, if $$U(\theta)=e^{i0.7\theta \sigma_x}$$, then $$\sigma_x$$, with scaling factor $$s$$, is the generator of operator $$U(\theta)$$:

generator = [PauliX, 0.7]


Default is [None, 1], indicating the operation has no generator.

grad_method

• 'A': analytic differentiation using the parameter-shift method.

• 'F': finite difference numerical differentiation.

• None: the operation may not be differentiated.

Default is 'F', or None if the Operation has zero parameters.

grad_recipe = None

Gradient recipe for the parameter-shift method.

This is a tuple with one nested list per operation parameter. For parameter $$\phi_k$$, the nested list contains elements of the form $$[c_i, a_i, s_i]$$ where $$i$$ is the index of the term, resulting in a gradient recipe of

$\frac{\partial}{\partial\phi_k}f = \sum_{i} c_i f(a_i \phi_k + s_i).$

If None, the default gradient recipe containing the two terms $$[c_0, a_0, s_0]=[1/2, 1, \pi/2]$$ and $$[c_1, a_1, s_1]=[-1/2, 1, -\pi/2]$$ is assumed for every parameter.

Type

tuple(Union(list[list[float]], None)) or None

inverse

Boolean determining if the inverse of the operation was requested.

matrix
name

Get and set the name of the operator.

num_params = 1
num_wires = -1
par_domain = 'A'
parameters

Current parameter values.

string_for_inverse = '.inv'
wires

Wires of this operator.

Returns

wires

Return type

Wires

 adjoint([do_queue]) Create an operation that is the adjoint of this one. decomposition(*params, wires) Returns a template decomposing the operation into other quantum operations. Returns a tape containing the decomposed operations, rather than a list. get_parameter_shift(idx[, shift]) Multiplier and shift for the given parameter, based on its gradient recipe. Inverts the operation, such that the inverse will be used for the computations by the specific device. Append the operator to the Operator queue.
adjoint(do_queue=False)

Create an operation that is the adjoint of this one.

Adjointed operations are the conjugated and transposed version of the original operation. Adjointed ops are equivalent to the inverted operation for unitary gates.

Parameters

do_queue – Whether to add the adjointed gate to the context queue.

Returns

static decomposition(*params, wires)

Returns a template decomposing the operation into other quantum operations.

expand()[source]

Returns a tape containing the decomposed operations, rather than a list.

Returns

Returns a quantum tape that contains the operations decomposition, or if not implemented, simply the operation itself.

Return type

JacobianTape

get_parameter_shift(idx, shift=1.5707963267948966)

Multiplier and shift for the given parameter, based on its gradient recipe.

Parameters

idx (int) – parameter index

Returns

multiplier, shift

Return type

float, float

inv()

Inverts the operation, such that the inverse will be used for the computations by the specific device.

This method concatenates a string to the name of the operation, to indicate that the inverse will be used for computations.

Any subsequent call of this method will toggle between the original operation and the inverse of the operation.

Returns

operation to be inverted

Return type

Operator

queue()

Append the operator to the Operator queue.