qiskit-documentation/docs/api/qiskit/0.32/qiskit.aqua.algorithms.QGAN.mdx

---
title: QGAN
description: API reference for qiskit.aqua.algorithms.QGAN
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.aqua.algorithms.QGAN
---

# QGAN

<Class id="qiskit.aqua.algorithms.QGAN" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/algorithms/distribution_learners/qgan.py" signature="QGAN(data, bounds=None, num_qubits=None, batch_size=500, num_epochs=3000, seed=7, discriminator=None, generator=None, tol_rel_ent=None, snapshot_dir=None, quantum_instance=None)" modifiers="class">
  Bases: `qiskit.aqua.algorithms.quantum_algorithm.QuantumAlgorithm`

  The Quantum Generative Adversarial Network algorithm.

  The qGAN \[1] is a hybrid quantum-classical algorithm used for generative modeling tasks.

  This adaptive algorithm uses the interplay of a generative [`GenerativeNetwork`](qiskit.aqua.components.neural_networks.GenerativeNetwork "qiskit.aqua.components.neural_networks.GenerativeNetwork") and a discriminative [`DiscriminativeNetwork`](qiskit.aqua.components.neural_networks.DiscriminativeNetwork "qiskit.aqua.components.neural_networks.DiscriminativeNetwork") network to learn the probability distribution underlying given training data.

  These networks are trained in alternating optimization steps, where the discriminator tries to differentiate between training data samples and data samples from the generator and the generator aims at generating samples which the discriminator classifies as training data samples. Eventually, the quantum generator learns the training data’s underlying probability distribution. The trained quantum generator loads a quantum state which is a model of the target distribution.

  **References:**

  **\[1] Zoufal et al.,**

  [Quantum Generative Adversarial Networks for learning and loading random distributions](https://www.nature.com/articles/s41534-019-0223-2)

  **Parameters**

  *   **data** (`Union`\[`ndarray`, `List`]) – Training data of dimension k
  *   **bounds** (`Union`\[`ndarray`, `List`, `None`]) – k min/max data values \[\[min\_0,max\_0],…,\[min\_k-1,max\_k-1]] if univariate data: \[min\_0,max\_0]
  *   **num\_qubits** (`Union`\[`ndarray`, `List`, `None`]) – k numbers of qubits to determine representation resolution, i.e. n qubits enable the representation of 2\*\*n values \[num\_qubits\_0,…, num\_qubits\_k-1]
  *   **batch\_size** (`int`) – Batch size, has a min. value of 1.
  *   **num\_epochs** (`int`) – Number of training epochs
  *   **seed** (`int`) – Random number seed
  *   **discriminator** (`Optional`\[`DiscriminativeNetwork`]) – Discriminates between real and fake data samples
  *   **generator** (`Optional`\[`GenerativeNetwork`]) – Generates ‘fake’ data samples
  *   **tol\_rel\_ent** (`Optional`\[`float`]) – Set tolerance level for relative entropy. If the training achieves relative entropy equal or lower than tolerance it finishes.
  *   **snapshot\_dir** (`Optional`\[`str`]) – Directory in to which to store cvs file with parameters, if None (default) then no cvs file is created.
  *   **quantum\_instance** (`Union`\[`QuantumInstance`, `Backend`, `BaseBackend`, `None`]) – Quantum Instance or Backend

  **Raises**

  [**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – invalid input

  ## Methods

  ### get\_rel\_entr

  <Function id="qiskit.aqua.algorithms.QGAN.get_rel_entr" signature="QGAN.get_rel_entr()">
    Get relative entropy between target and trained distribution

    **Return type**

    `float`
  </Function>

  ### run

  <Function id="qiskit.aqua.algorithms.QGAN.run" signature="QGAN.run(quantum_instance=None, **kwargs)">
    Execute the algorithm with selected backend.

    **Parameters**

    *   **quantum\_instance** (`Union`\[`QuantumInstance`, `Backend`, `BaseBackend`, `None`]) – the experimental setting.
    *   **kwargs** (*dict*) – kwargs

    **Returns**

    results of an algorithm.

    **Return type**

    dict

    **Raises**

    [**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – If a quantum instance or backend has not been provided
  </Function>

  ### set\_backend

  <Function id="qiskit.aqua.algorithms.QGAN.set_backend" signature="QGAN.set_backend(backend, **kwargs)">
    Sets backend with configuration.

    **Return type**

    `None`
  </Function>

  ### set\_discriminator

  <Function id="qiskit.aqua.algorithms.QGAN.set_discriminator" signature="QGAN.set_discriminator(discriminator=None)">
    Initialize discriminator.

    **Parameters**

    **discriminator** (*Discriminator*) – discriminator
  </Function>

  ### set\_generator

  <Function id="qiskit.aqua.algorithms.QGAN.set_generator" signature="QGAN.set_generator(generator_circuit=None, generator_init_params=None, generator_optimizer=None, generator_gradient=None)">
    Initialize generator.

    **Parameters**

    *   **generator\_circuit** (`Union`\[`UnivariateVariationalDistribution`, `MultivariateVariationalDistribution`, `QuantumCircuit`, `None`]) – parameterized quantum circuit which sets the structure of the quantum generator
    *   **generator\_init\_params** (`Optional`\[`ndarray`]) – initial parameters for the generator circuit
    *   **generator\_optimizer** (`Optional`\[`Optimizer`]) – optimizer to be used for the training of the generator
    *   **generator\_gradient** (`Union`\[`Callable`, `Gradient`, `None`]) – A Gradient object, or a function returning partial derivatives of the loss function w\.r.t. the generator variational params.

    **Raises**

    [**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – invalid input
  </Function>

  ### train

  <Function id="qiskit.aqua.algorithms.QGAN.train" signature="QGAN.train()">
    Train the qGAN

    **Raises**

    [**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – Batch size bigger than the number of items in the truncated data set
  </Function>

  ## Attributes

  ### backend

  <Attribute id="qiskit.aqua.algorithms.QGAN.backend">
    Returns backend.

    **Return type**

    `Union`\[`Backend`, `BaseBackend`]
  </Attribute>

  ### d\_loss

  <Attribute id="qiskit.aqua.algorithms.QGAN.d_loss">
    Returns discriminator loss

    **Return type**

    `List`\[`float`]
  </Attribute>

  ### discriminator

  <Attribute id="qiskit.aqua.algorithms.QGAN.discriminator">
    Returns discriminator
  </Attribute>

  ### g\_loss

  <Attribute id="qiskit.aqua.algorithms.QGAN.g_loss">
    Returns generator loss

    **Return type**

    `List`\[`float`]
  </Attribute>

  ### generator

  <Attribute id="qiskit.aqua.algorithms.QGAN.generator">
    Returns generator
  </Attribute>

  ### quantum\_instance

  <Attribute id="qiskit.aqua.algorithms.QGAN.quantum_instance">
    Returns quantum instance.

    **Return type**

    `Optional`\[`QuantumInstance`]
  </Attribute>

  ### random

  <Attribute id="qiskit.aqua.algorithms.QGAN.random">
    Return a numpy random.
  </Attribute>

  ### rel\_entr

  <Attribute id="qiskit.aqua.algorithms.QGAN.rel_entr">
    Returns relative entropy between target and trained distribution

    **Return type**

    `List`\[`float`]
  </Attribute>

  ### seed

  <Attribute id="qiskit.aqua.algorithms.QGAN.seed">
    Returns random seed
  </Attribute>

  ### tol\_rel\_ent

  <Attribute id="qiskit.aqua.algorithms.QGAN.tol_rel_ent">
    Returns tolerance for relative entropy
  </Attribute>
</Class>