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class qiskit.circuit.library.LinearAmplitudeFunction(num_state_qubits, slope, offset, domain, image, rescaling_factor=1, breakpoints=None, name='F')

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Bases: QuantumCircuit

A circuit implementing a (piecewise) linear function on qubit amplitudes.

An amplitude function FF of a function ff is a mapping

Fx0=1f^(x)x0+f^(x)x1.F|x\rangle|0\rangle = \sqrt{1 - \hat{f}(x)} |x\rangle|0\rangle + \sqrt{\hat{f}(x)} |x\rangle|1\rangle.

for a function f^:{0,...,2n1}[0,1]\hat{f}: \{ 0, ..., 2^n - 1 \} \rightarrow [0, 1], where x|x\rangle is a nn qubit state.

This circuit implements FF for piecewise linear functions f^\hat{f}. In this case, the mapping FF can be approximately implemented using a Taylor expansion and linearly controlled Pauli-Y rotations, see [1, 2] for more detail. This approximation uses a rescaling_factor to determine the accuracy of the Taylor expansion.

In general, the function of interest ff is defined from some interval [a,b][a,b], the domain to [c,d][c,d], the image, instead of {1,...,N}\{ 1, ..., N \} to [0,1][0, 1]. Using an affine transformation we can rescale ff to f^\hat{f}:

f^(x)=f(ϕ(x))cdc\hat{f}(x) = \frac{f(\phi(x)) - c}{d - c}


ϕ(x)=a+ba2n1x.\phi(x) = a + \frac{b - a}{2^n - 1} x.

If ff is a piecewise linear function on mm intervals [pi1,pi],i{1,...,m}[p_{i-1}, p_i], i \in \{1, ..., m\} with slopes αi\alpha_i and offsets βi\beta_i it can be written as

f(x)=i=1m1[pi1,pi](x)(αix+βi)f(x) = \sum_{i=1}^m 1_{[p_{i-1}, p_i]}(x) (\alpha_i x + \beta_i)

where 1[a,b]1_{[a, b]} is an indication function that is 1 if the argument is in the interval [a,b][a, b] and otherwise 0. The breakpoints pip_i can be specified by the breakpoints argument.


[1]: Woerner, S., & Egger, D. J. (2018).

Quantum Risk Analysis. arXiv:1806.06893(opens in a new tab)

[2]: Gacon, J., Zoufal, C., & Woerner, S. (2020).

Quantum-Enhanced Simulation-Based Optimization. arXiv:2005.10780(opens in a new tab)




Returns a list of ancilla bits in the order that the registers were added.


Return calibration dictionary.

The custom pulse definition of a given gate is of the form {'gate_name': {(qubits, params): schedule}}


Returns a list of classical bits in the order that the registers were added.


Return the circuit data (instructions and context).


a list-like object containing the CircuitInstructions for each instruction.

Return type



Default value: 'include "";'


Return the global phase of the current circuit scope in radians.

Default value: 'OPENQASM 2.0;'


Default value: 181


Return any associated layout information about the circuit

This attribute contains an optional TranspileLayout object. This is typically set on the output from transpile() or to retain information about the permutations caused on the input circuit by transpilation.

There are two types of permutations caused by the transpile() function, an initial layout which permutes the qubits based on the selected physical qubits on the Target, and a final layout which is an output permutation caused by SwapGates inserted during routing.


The user provided metadata associated with the circuit.

The metadata for the circuit is a user provided dict of metadata for the circuit. It will not be used to influence the execution or operation of the circuit, but it is expected to be passed between all transforms of the circuit (ie transpilation) and that providers will associate any circuit metadata with the results it returns from execution of that circuit.


Return the number of ancilla qubits.


Return number of classical bits.


The number of parameter objects in the circuit.


Return number of qubits.


Return a list of operation start times.

This attribute is enabled once one of scheduling analysis passes runs on the quantum circuit.


List of integers representing instruction start times. The index corresponds to the index of instruction in


AttributeError(opens in a new tab) – When circuit is not scheduled.


The parameters defined in the circuit.

This attribute returns the Parameter objects in the circuit sorted alphabetically. Note that parameters instantiated with a ParameterVector are still sorted numerically.


The snippet below shows that insertion order of parameters does not matter.

>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> a, b, elephant = Parameter("a"), Parameter("b"), Parameter("elephant")
>>> circuit = QuantumCircuit(1)
>>> circuit.rx(b, 0)
>>> circuit.rz(elephant, 0)
>>> circuit.ry(a, 0)
>>> circuit.parameters  # sorted alphabetically!
ParameterView([Parameter(a), Parameter(b), Parameter(elephant)])

Bear in mind that alphabetical sorting might be unintuitive when it comes to numbers. The literal “10” comes before “2” in strict alphabetical sorting.

>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> angles = [Parameter("angle_1"), Parameter("angle_2"), Parameter("angle_10")]
>>> circuit = QuantumCircuit(1)
>>> circuit.u(*angles, 0)
>>> circuit.draw()
>>> circuit.parameters
ParameterView([Parameter(angle_1), Parameter(angle_10), Parameter(angle_2)])

To respect numerical sorting, a ParameterVector can be used.

>>> from qiskit.circuit import QuantumCircuit, Parameter, ParameterVector
>>> x = ParameterVector("x", 12)
>>> circuit = QuantumCircuit(1)
>>> for x_i in x:
...     circuit.rx(x_i, 0)
>>> circuit.parameters
    ParameterVectorElement(x[0]), ParameterVectorElement(x[1]),
    ParameterVectorElement(x[2]), ParameterVectorElement(x[3]),
    ..., ParameterVectorElement(x[11])


The sorted Parameter objects in the circuit.


Default value: 'circuit'


Returns a list of quantum bits in the order that the registers were added.




Map the function value of the approximated f^\hat{f} to ff.


scaled_value (float(opens in a new tab)) – A function value from the Taylor expansion of f^(x)\hat{f}(x).


The scaled_value mapped back to the domain of ff, by first inverting the transformation used for the Taylor approximation and then mapping back from [0,1][0, 1] to the original domain.

Return type

float(opens in a new tab)

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