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---
title: ReverseQGT
description: API reference for qiskit.algorithms.gradients.ReverseQGT
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.algorithms.gradients.ReverseQGT
---
# ReverseQGT
<Class id="qiskit.algorithms.gradients.ReverseQGT" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.23/qiskit/algorithms/gradients/reverse_gradient/reverse_qgt.py" signature="ReverseQGT(phase_fix=True, derivative_type=DerivativeType.COMPLEX)" modifiers="class">
Bases: [`qiskit.algorithms.gradients.base_qgt.BaseQGT`](qiskit.algorithms.gradients.BaseQGT "qiskit.algorithms.gradients.base_qgt.BaseQGT")
QGT calculation with the classically efficient reverse mode.
<Admonition title="Note" type="note">
This QGT implementation is based on statevector manipulations and scales exponentially with the number of qubits. However, for small system sizes it can be very fast compared to circuit-based gradients.
</Admonition>
This class implements the calculation of the QGT as described in \[1]. By keeping track of three statevectors and iteratively sweeping through each parameterized gate, this method scales only quadratically with the number of parameters.
**References:**
> **\[1]: Jones, T. “Efficient classical calculation of the Quantum Natural Gradient” (2020).**
>
> [arXiv:2011.02991](https://arxiv.org/abs/2011.02991).
**Parameters**
* **phase\_fix** (`bool`) Whether or not to include the phase fix.
* **derivative\_type** ([`DerivativeType`](qiskit.algorithms.gradients.DerivativeType "qiskit.algorithms.gradients.utils.DerivativeType")) Determines whether the complex QGT or only the real or imaginary parts are calculated.
## Methods
### run
<Function id="qiskit.algorithms.gradients.ReverseQGT.run" signature="ReverseQGT.run(circuits, parameter_values, parameters=None, **options)">
Run the job of the QGTs on the given circuits.
**Parameters**
* **circuits** The list of quantum circuits to compute the QGTs.
* **parameter\_values** The list of parameter values to be bound to the circuit.
* **parameters** The sequence of parameters to calculate only the QGTs of the specified parameters. Each sequence of parameters corresponds to a circuit in `circuits`. Defaults to None, which means that the QGTs of all parameters in each circuit are calculated.
* **options** Primitive backend runtime options used for circuit execution. The order of priority is: options in `run` method > QGTs default options > primitives default setting. Higher priority setting overrides lower priority setting.
**Returns**
The job object of the QGTs of the expectation values. The i-th result corresponds to `circuits[i]` evaluated with parameters bound as `parameter_values[i]`.
**Raises**
**ValueError** Invalid arguments are given.
</Function>
### update\_default\_options
<Function id="qiskit.algorithms.gradients.ReverseQGT.update_default_options" signature="ReverseQGT.update_default_options(**options)">
Update the gradients default options setting.
**Parameters**
**\*\*options** The fields to update the default options.
</Function>
## Attributes
### SUPPORTED\_GATES
<Attribute id="qiskit.algorithms.gradients.ReverseQGT.SUPPORTED_GATES" attributeValue="['rx', 'ry', 'rz', 'cp', 'crx', 'cry', 'crz']" />
### derivative\_type
<Attribute id="qiskit.algorithms.gradients.ReverseQGT.derivative_type">
The derivative type.
**Return type**
[`DerivativeType`](qiskit.algorithms.gradients.DerivativeType "qiskit.algorithms.gradients.utils.DerivativeType")
</Attribute>
### options
<Attribute id="qiskit.algorithms.gradients.ReverseQGT.options">
There are no options for the reverse QGT, returns an empty options dict.
**Return type**
[`Options`](qiskit.providers.Options "qiskit.providers.options.Options")
**Returns**
Empty options.
</Attribute>
</Class>