# Factory Objects¶

A Factory object is a self-contained representation of an error mitigation method.

This representation is not just hardware-agnostic, it is even quantum-agnostic, in the sense that it mainly deals with classical data: the classical input and the classical output of a noisy computation. Nonetheless, a factory can easily interact with a quantum system via its self.run method which is the only interface between the “classical world” of a factory and the “quantum world” of a circuit.

The typical tasks of a factory are:

1. Record the result of the computation executed at the chosen noise level;

2. Determine the noise scale factor at which the next computation should be run;

3. Given the history of noise scale factors and results (respectively stored in the object attributes self.instack and self.outstack), evaluate the associated zero-noise extrapolation.

The structure of the Factory class is adaptive by construction, since the choice of the next noise level can depend on the history of self.instack and self.outstack. Obviously, non-adaptive methods are supported too and they actually represent the most common choice.

Specific classes derived from the abstract class Factory, like LinearFactory, RichardsonFactory, etc., represent different zero-noise extrapolation methods. All the built-in factories can be found in the module mitiq.zne.inference and are summarized in the following table.

 mitiq.zne.inference.LinearFactory Factory object implementing zero-noise extrapolation based on a linear fit. mitiq.zne.inference.RichardsonFactory Factory object implementing Richardson’s extrapolation. mitiq.zne.inference.PolyFactory Factory object implementing a zero-noise extrapolation algorithm based on a polynomial fit. mitiq.zne.inference.ExpFactory Factory object implementing a zero-noise extrapolation algorithm assuming an exponential ansatz y(x) = a + b * exp(-c * x), with c > 0. mitiq.zne.inference.PolyExpFactory Factory object implementing a zero-noise extrapolation algorithm assuming an (almost) exponential ansatz with a non linear exponent, i.e.: mitiq.zne.inference.AdaExpFactory Factory object implementing an adaptive zero-noise extrapolation algorithm assuming an exponential ansatz y(x) = a + b * exp(-c * x), with c > 0.

Once instantiated, a factory can be passed as an argument to the high-level functions contained in the module mitiq.zne.zne. Alternatively, a factory can be directly used to implement a zero-noise extrapolation procedure in a fully self-contained way.

To clarify this aspect, we now perform the same zero-noise extrapolation with both methods.

## Using a factory object with the mitiq.zne module¶

Let us consider an executor function which is similar to the one used in the getting started section.

import numpy as np
from cirq import Circuit, depolarize, DensityMatrixSimulator

# initialize a backend
SIMULATOR = DensityMatrixSimulator()
# 5% depolarizing noise
NOISE = 0.05

def executor(circ: Circuit) -> float:
"""Executes a circuit with depolarizing noise and
returns the expectation value of the projector |0><0|."""
circuit = circ.with_noise(depolarize(p=NOISE))
rho = SIMULATOR.simulate(circuit).final_density_matrix
obs = np.diag([1, 0])
expectation = np.real(np.trace(rho @ obs))
return expectation


Note

In this example we used Cirq but other quantum software platforms can be used, as shown in the getting started section.

We also define a simple quantum circuit whose ideal expectation value is by construction equal to 1.0.

from cirq import LineQubit, X, H

qubit = LineQubit(0)
circuit = Circuit(X(qubit), H(qubit), H(qubit), X(qubit))
expval = executor(circuit)
exact = 1.0
print(f"The ideal result should be {exact}")
print(f"The real result is {expval:.4f}")
print(f"The abslute error is {abs(exact - expval):.4f}")

The ideal result should be 1.0
The real result is 0.8794
The abslute error is 0.1206


Now we are going to initialize three factory objects, each one encapsulating a different zero-noise extrapolation method.

from mitiq.zne.inference import LinearFactory, RichardsonFactory, PolyFactory

# method: scale noise by 1 and 2, then extrapolate linearly to the zero noise limit.
linear_fac = LinearFactory(scale_factors=[1.0, 2.0])

# method: scale noise by 1, 2 and 3, then evaluate the Richardson extrapolation.
richardson_fac = RichardsonFactory(scale_factors=[1.0, 2.0, 3.0])

# method: scale noise by 1, 2, 3, and 4, then extrapolate quadratically to the zero noise limit.
poly_fac = PolyFactory(scale_factors=[1.0, 2.0, 3.0, 4.0], order=2)


The previous factory objects can be passed as arguments to the high-level functions in mitiq.zne. For example:

from mitiq.zne.zne import execute_with_zne

zne_expval = execute_with_zne(circuit, executor, factory=linear_fac)
print(f"Error with linear_fac: {abs(exact - zne_expval):.4f}")

zne_expval = execute_with_zne(circuit, executor, factory=richardson_fac)
print(f"Error with richardson_fac: {abs(exact - zne_expval):.4f}")

zne_expval = execute_with_zne(circuit, executor, factory=poly_fac)
print(f"Error with poly_fac: {abs(exact - zne_expval):.4f}")

Error with linear_fac: 0.0291
Error with richardson_fac: 0.0070
Error with poly_fac: 0.0110


## Directly using a factory for error mitigation¶

Zero-noise extrapolation can also be implemented by directly using the methods self.run and self.reduce of a Factory object.

The method self.run evaluates different expectation values at different noise levels until a sufficient amount of data is collected.

The method self.reduce instead returns the final zero-noise extrapolation which, in practice, corresponds to a statistical inference based on the measured data.

# we import one of the built-in noise scaling function
from mitiq.zne.scaling import fold_gates_at_random

linear_fac.run(circuit, executor, scale_noise=fold_gates_at_random)
zne_expval = linear_fac.reduce()
print(f"Error with linear_fac: {abs(exact - zne_expval):.4f}")

richardson_fac.run(circuit, executor, scale_noise=fold_gates_at_random)
zne_expval = richardson_fac.reduce()
print(f"Error with richardson_fac: {abs(exact - zne_expval):.4f}")

poly_fac.run(circuit, executor, scale_noise=fold_gates_at_random)
zne_expval = poly_fac.reduce()
print(f"Error with poly_fac: {abs(exact - zne_expval):.4f}")

Error with linear_fac: 0.0291
Error with richardson_fac: 0.0070
Error with poly_fac: 0.0110


## Advanced usage of a factory¶

Note

This section can be safely skipped by all the readers who are interested in a standard usage of mitiq. On the other hand, more experienced users and mitiq contributors may find this content useful to understand how a factory object actually works at a deeper level.

In this advanced section we present a low-level usage and a very-low-level usage of a factory. Again, for simplicity, we solve the same zero-noise extrapolation problem that we have just considered in the previous sections.

Eventually we will also discuss how the user can easily define a custom factory class.

### Low-level usage: the iterate method.¶

The self.run method takes as arguments a circuit and other “quantum” objects. On the other hand, the core computation performed by any factory corresponds to a some classical computation applied to the measurement results.

At a lower level, it is possible to clearly separate the quantum and the classical steps of a zero-noise extrapolation procedure. This can be done by defining a function which maps a noise scale factor to the corresponding expectation value.

def noise_to_expval(scale_factor: float) -> float:
"""Function returning an expectation value for a given scale_factor."""
# apply noise scaling
scaled_circuit = fold_gates_at_random(circuit, scale_factor)
# return the corresponding expectation value
return executor(scaled_circuit)


Note

The body of the previous function contains the execution of a quantum circuit. However, if we see it as a “black-box”, it is just a classical function mapping real numbers to real numbers.

The function noise_to_expval encapsulate the “quantum part” of the problem. The “classical part” of the problem can be solved by passing noise_to_expval to the self.iterate method of a factory object. This method will repeatedly call noise_to_expval for different noise levels until a sufficient amount of data is collected. So, one can view self.iterate as the classical counterpart of the quantum method self.run.

linear_fac.iterate(noise_to_expval)
zne_expval = linear_fac.reduce()
print(f"Error with linear_fac: {abs(exact - zne_expval):.4f}")

richardson_fac.iterate(noise_to_expval)
zne_expval = richardson_fac.reduce()
print(f"Error with richardson_fac: {abs(exact - zne_expval):.4f}")

poly_fac.iterate(noise_to_expval)
zne_expval = poly_fac.reduce()
print(f"Error with poly_fac: {abs(exact - zne_expval):.4f}")

Error with linear_fac: 0.0291
Error with richardson_fac: 0.0070
Error with poly_fac: 0.0110


Note

With respect to self.run the self.iterate method is much more flexible and can be applied whenever the user is able to autonomously scale the noise level associated to an expectation value. Indeed, the function noise_to_expval can represent any experiment or any simulation in which noise can be artificially increased. The scenario is therefore not restricted to quantum circuits but can be easily extended to annealing devices or to gates which are controllable at a pulse level. In principle, one could even use the self.iterate method to mitigate experiments which are unrelated to quantum computing.

### Very low-level usage of a factory¶

It is also possible to emulate the action of the self.iterate method by manually measuring individual expectation values and saving them, one by one, into the factory.

Note

In a typical situation, such a deep level of control is likely unnecessary. It is anyway instructive to understand the internal structure of the Factory class, especially if one is interested in defining a custom factory.

zne_list = []
# loop over different factories
for fac in [linear_fac, richardson_fac, poly_fac]:
# loop until enough expectation values are measured
while not fac.is_converged():
# Get the next noise scale factor from the factory
next_scale_factor = fac.next()
# Evaluate the expectation value
expval = noise_to_expval(next_scale_factor)
# Save the noise scale factor and the result into the factory
fac.push(next_scale_factor, expval)
# evaluate the zero-noise limit and append it to zne_list
zne_list.append(fac.reduce())

print(f"Error with linear_fac: {abs(exact - zne_list[0]):.4f}")
print(f"Error with richardson_fac: {abs(exact - zne_list[1]):.4f}")
print(f"Error with poly_fac: {abs(exact - zne_list[2]):.4f}")

Error with linear_fac: 0.0291
Error with richardson_fac: 0.0070
Error with poly_fac: 0.0110


In the previous code block we used the some core methods of a Factory object:

• self.next to get the next noise scale factor;

• self.push to save the measured data into the factory;

• self.is_converged to know if enough data has been collected.

### Defining a custom factory¶

If necessary, the user can modify an existing extrapolation methods by subclassing one of the built-in factories.

Alternatively, a new adaptive extrapolation method can be derived from the abstract class Factory. In this case its core methods must be implemented: self.next, self.push, self.is_converged, self.reduce, etc. Typically, the self.__init__ method must be overridden.

A new non-adaptive method can instead be derived from the abstract BatchedFactory class. In this case it is usually sufficient to override only the self.__init__ and the self.reduce methods, which are responsible for the initialization and for the final zero-noise extrapolation, respectively.

#### Example: a simple custom factory¶

Assume that, from physical considerations, we know that the ideal expectation value (measured by some quantum circuit) must always be within two limits: min_expval and max_expval. For example, this is a typical situation whenever the measured observable has a bounded spectrum.

We can define a linear non-adaptive factory which takes into account this information and clips the result if it falls outside its physical domain.

from typing import Iterable
from mitiq.zne.inference import BatchedFactory, mitiq_polyfit
import numpy as np

class MyFactory(BatchedFactory):
"""Factory object implementing a linear extrapolation taking
into account that the expectation value must be within a given
interval. If the zero-noise limit falls outside the
interval, its value is clipped.
"""

def __init__(
self,
scale_factors: Iterable[float],
min_expval: float,
max_expval: float,
) -> None:
"""
Args:
scale_factors: The noise scale factors at which
expectation values should be measured.
min_expval: The lower bound for the expectation value.
min_expval: The upper bound for the expectation value.
"""
super(MyFactory, self).__init__(scale_factors)
self.min_expval = min_expval
self.max_expval = max_expval

def reduce(self) -> float:
"""
Fit a linear model and clip its zero-noise limit.

Returns:
The clipped extrapolation to the zero-noise limit.
"""
# Fit a line and get the intercept
scale_factors = [params["scale_factor"] for params in self.instack]
_, intercept = mitiq_polyfit(scale_factors, self.outstack, deg=1)

# Return the clipped zero-noise extrapolation.
return np.clip(intercept, self.min_expval, self.max_expval)


This custom factory can be used in exactly the same way as we have shown in the previous section. By simply replacing LinearFactory with MyFactory in all the previous code snippets, the new extrapolation method will be applied.

#### Regression tools in mitiq.zne.inference¶

In the body of the previous MyFactory example, we imported and used the mitiq_polyfit() function. This is simply a wrap of numpy.polyfit(), slightly adapted to the notion and to the error types of mitiq. This function can be used to fit a polynomial ansatz to the measured expectation values. This function performs a least squares minimization which is linear (with respect to the coefficients) and therefore admits an algebraic solution.

Similarly, from mitiq.zne.inference one can also import mitiq_curve_fit(), which is instead a wrap of scipy.optimize.curve_fit(). Differently from mitiq_polyfit(), mitiq_curve_fit() can be used with a generic (user-defined) ansatz. Since the fit is based on a numerical non-linear least squares minimization, this method may fail to converge or could be subject to numerical instabilities.