# Hessian

*class *`qiskit.opflow.gradients.Hessian(hess_method='param_shift', **kwargs)`

Bases: `HessianBase`

Deprecated: Compute the Hessian of an expected value.

Deprecated since version 0.24.0

The class `qiskit.opflow.gradients.hessian.Hessian`

is deprecated as of qiskit-terra 0.24.0. It will be removed in the Qiskit 1.0 release. For code migration guidelines, visit https://qisk.it/opflow_migration(opens in a new tab).

## Attributes

### hess_method

Returns `CircuitGradient`

.

**Returns**

`CircuitGradient`

.

## Methods

### convert

`convert(operator, params=None)`

**Parameters**

**operator**(*OperatorBase*) – The operator for which we compute the Hessian**params**(*Tuple*(opens in a new tab)*[**ParameterExpression**,**ParameterExpression**] |**List*(opens in a new tab)*[**Tuple*(opens in a new tab)*[**ParameterExpression**,**ParameterExpression**]] |**List*(opens in a new tab)*[**ParameterExpression**] |**ParameterVector**| None*) – The parameters we are computing the Hessian with respect to Either give directly the tuples/list of tuples for which the second order derivative is to be computed or give a list of parameters to build the full Hessian for those parameters. If not explicitly passed, the full Hessian is constructed. The parameters are then inferred from the operator and sorted by name.

**Returns**

An operator whose evaluation yields the Hessian

**Return type**

### get_hessian

`get_hessian(operator, params=None)`

Get the Hessian for the given operator w.r.t. the given parameters

**Parameters**

**operator**(*OperatorBase*) – Operator w.r.t. which we take the Hessian.**params**(*Tuple*(opens in a new tab)*[**ParameterExpression**,**ParameterExpression**] |**List*(opens in a new tab)*[**Tuple*(opens in a new tab)*[**ParameterExpression**,**ParameterExpression**]] |**List*(opens in a new tab)*[**ParameterExpression**] |**ParameterVector**| None*) – Parameters w.r.t. which we compute the Hessian. If not explicitly passed, the full Hessian is constructed. The parameters are then inferred from the operator and sorted by name.

**Returns**

Operator which represents the gradient w.r.t. the given params.

**Raises**

**ValueError**(opens in a new tab) – If`params`

contains a parameter not present in`operator`

.**ValueError**(opens in a new tab) – If`operator`

is not parameterized.**OpflowError**– If the coefficient of the operator could not be reduced to 1.**OpflowError**– If the differentiation of a combo_fn requires JAX but the package is not installed.**TypeError**(opens in a new tab) – If the operator does not include a StateFn given by a quantum circuit**TypeError**(opens in a new tab) – If the parameters were given in an unsupported format.**Exception**(opens in a new tab) – Unintended code is reached**MissingOptionalLibraryError**– jax not installed

**Return type**

Was this page helpful?

Report a bug or request content on GitHub.