Models

qrobot.models module class diagram:

The Model abstract class

This class is the parent class which defines the general features a perception model has to implement. It is used to define custom models as a child classes:

from qrobot.models import Model

class myModel(Model):
    (...)

class qrobot.models.Model(n, tau)

Model is an abstract class which embeds the general features needed in a model for QL perception.

Parameters

• n (int) – Model’s dimension ( must be greater than 0)

• tau (int) – Number of samples of the temporal window (must be greater than 0)

n

Model’s dimension.

Type

int

tau

Number of samples of the temporal window.

Type

int

circ

Quantum circuit which implements the model.

Type

qiskit.QuantumCircuit

clear()

Re-initialize the model with an empty circuit.

abstract decode()

Exploits the information encoded in the qubit (abstract class).

abstract encode(input, dim)

Encodes the input in the correspondent qubit.

Example

To encode a sequence of input vectors, given tau and n:

for t in range(0,model.tau): # loop through time
    for dim in range(1, model.n + 1): # loop through dimensions
        model.encode(sequence[t][dim-1], dim)

get_density()

Returns the simulated density matrix of the model.

Returns

Model’s density matrix.

Return type

numpy.ndarray

get_state()

Returns the simulated state vector of the model.

Returns

Model’s state vector.

Return type

numpy.ndarray

measure(shots=1, backend=QasmSimulator(backend_name='qasm_simulator', provider=AerProvider()))

Measures the qubits using a IBMQ backend

Parameters

• shots (int) – Number of measurement shots

• backend (qiskit backend) – Quantum backend for the execution (QASM simulator as default)

Returns

State occurrences counts in the form {“state”: count}

Return type

dict

plot_state_mat()

Plots the state and density matrix of the quantum system (just the real parts).

Example

To plot a perfectly balanced superposition of states:

model = Model(n, tau) # change Model with the desired child class

for t in range(0,model.tau): # loop through time
    for dim in range(1, model.n + 1): # loop through dimensions
        model.encode(.5, dim)

model.plot_state_mat()

Raises

OverflowError – If the dimension of the model is 6 or greater, plotting fails due to the high number of basis states.

print_circuit()

Prints the quantum circuit on which the model is implemented.

abstract query(target)

Changes the basis of the quantum system choosing target as the basis state |00…0>.

The AngularModel class

This is a custom Model provided by the package. This model encodes the perceptual information in the angle of the qubits’ Bloch sphere representations.

class qrobot.models.AngularModel(n, tau)

AngularModel is a kind of Model which encodes the perceptual information in the angle of the qubits’ Bloch sphere representations

decode()

The decoding for the AngularModel is a single measurement.

encode(input, dim)

Encodes the input in the correspondent qubit

Parameters

• input (float) – The scalar input for a certain dimension, must be a number between 0 and 1 inclusive.

• dim (int) – The model’s dimension which the input belongs.

Returns

The rotation angle applied to the qubit.

Return type

float

query(target)

Changes the basis of the quantum system choosing target as the basis state |00…0>

Parameters

target (list) – The target state, it must be a list containing n floats (between 0 and 1 inclusive).

The LinearModel class

This is a custom model derived from AngularModel, which corrects it with a nonlinear encoding. By means of its nonlinear encoding, this model provides a linear decoding (a notebook is provided in order to illustrate the difference between this and the angular one).

class qrobot.models.LinearModel(n, tau)

LinearModel corrects the AngularModel the encoding, allowing a linear decoding for single-event sequencies (tau=1).

Notes

The current implementation of LinearModel provides a linear dependency between encoded input and decoding probabilities only for tau = 1 (or for tau > 1 if and only if the input is always the same one). For a time-varying sequencee (tau > 1) the results obtained are again nonlinear, and similar to the one of AngularModel (see this notebook for more information)

decode()

The decoding for the LinearModel is a single measurement.

encode(input, dim)

Encodes the input in the correspondent qubit

Parameters

• input (float) – The scalar input for a certain dimension, must be a number between 0 and 1 inclusive.

• dim (int) – The model’s dimension which the input belongs.

Returns

The rotation angle applied to the qubit.

Return type

float

query(target)

Same query function as the AngularModel

See also

AngularModel.query

The BurstAModel class

This is a modified AngularModel which provides a burst instead of a dictionary as decoding, which is the ration of qubits recorded in a |0> state out of the dimension of the mode:

>>> AngularModel.decode(model)
{"state" = 1}
>>> BurstAModel.decode(model)
float

# E.g.
>>> AngularModel.decode(model)
{"111100" = 1}
>>> BurstAModel.decode(model)
2/6 = 0.2

class qrobot.models.BurstAModel(n, tau)

BurstAModel is a kind of AngularModel which returns a float, rather than a dictionary, as the decoded output.

decode()

The decoding for the BurstAModel is “burst” between 0 and 1, which corresponds to the sum of the “zeros” in the measured state divided by the dimension n of the model. For example, if the measure is {“11100” = 1} the decoded burst is 2/6 = 0.333333333333

Returns

The burst, a value between 0 and 1 (the nearer to 0, the closer to the basis state |00…0> )

Return type

float

Module variables

qrobot.models.model.QASM_BACKEND = QasmSimulator( backend_name='qasm_simulator', provider=AerProvider())

Module-level qiskit backend variable for quantum circuit simulation on local classical hardware via QASM simulator.

Type

qiskit backend