---
title: StatevectorEstimator (latest version)
description: API reference for qiskit.primitives.StatevectorEstimator in the latest version of qiskit
source: https://eu-de.quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.primitives.StatevectorEstimator
---

# StatevectorEstimator

*class* `qiskit.primitives.StatevectorEstimator(*, default_precision=0.0, seed=None)`

[GitHub](https://github.com/Qiskit/qiskit/tree/stable/2.5/qiskit/primitives/statevector_estimator.py#L31-L176)

Bases: [`BaseEstimatorV2`](/docs/api/qiskit/qiskit.primitives.BaseEstimatorV2 "qiskit.primitives.base.base_estimator.BaseEstimatorV2")

Simple implementation of [`BaseEstimatorV2`](/docs/api/qiskit/qiskit.primitives.BaseEstimatorV2 "qiskit.primitives.BaseEstimatorV2") with full state vector simulation.

This class is implemented via [`Statevector`](/docs/api/qiskit/qiskit.quantum_info.Statevector "qiskit.quantum_info.Statevector") which turns provided circuits into pure state vectors. These states are subsequently acted on by [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp "qiskit.quantum_info.SparsePauliOp"), which implies that, at present, this implementation is only compatible with Pauli-based observables.

Each tuple of `(circuit, observables, <optional> parameter values, <optional> precision)`, called an estimator primitive unified bloc (PUB), produces its own array-based result. The [`run()`](#qiskit.primitives.StatevectorEstimator.run "qiskit.primitives.StatevectorEstimator.run") method can be given a sequence of pubs to run in one call.

> **Note**
>
> The result of this class is exact if the circuit contains only unitary operations. On the other hand, the result could be stochastic if the circuit contains a non-unitary operation such as a reset for some subsystems. The stochastic result can be made reproducible by setting `seed`, e.g., `StatevectorEstimator(seed=123)`.

```python
from qiskit.circuit import Parameter, QuantumCircuit
from qiskit.primitives import StatevectorEstimator
from qiskit.quantum_info import Pauli, SparsePauliOp

import matplotlib.pyplot as plt
import numpy as np

# Define a circuit with two parameters.
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.ry(Parameter("a"), 0)
circuit.rz(Parameter("b"), 0)
circuit.cx(0, 1)
circuit.h(0)

# Define a sweep over parameter values, where the second axis is over
# the two parameters in the circuit.
params = np.vstack([
    np.linspace(-np.pi, np.pi, 100),
    np.linspace(-4 * np.pi, 4 * np.pi, 100)
]).T

# Define three observables. Many formats are supported here including
# classes such as qiskit.quantum_info.SparsePauliOp. The inner length-1
# lists cause this array of observables to have shape (3, 1), rather
# than shape (3,) if they were omitted.
observables = [
    [SparsePauliOp(["XX", "IY"], [0.5, 0.5])],
    [Pauli("XX")],
    [Pauli("IY")]
]

# Instantiate a new statevector simulation based estimator object.
estimator = StatevectorEstimator()

# Estimate the expectation value for all 300 combinations of
# observables and parameter values, where the pub result will have
# shape (3, 100). This shape is due to our array of parameter
# bindings having shape (100,), combined with our array of observables
# having shape (3, 1)
pub = (circuit, observables, params)
job = estimator.run([pub])

# Extract the result for the 0th pub (this example only has one pub).
result = job.result()[0]

# Error-bar information is also available, but the error is 0
# for this StatevectorEstimator.
result.data.stds

# Pull out the array-based expectation value estimate data from the
# result and plot a trace for each observable.
for idx, pauli in enumerate(observables):
    plt.plot(result.data.evs[idx], label=pauli)
plt.legend()
```

![Output from the previous code.](https://eu-de.quantum.cloud.ibm.com/docs/images/api/qiskit/qiskit-primitives-StatevectorEstimator-1.avif)

**Parameters**

- **default\_precision** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The default precision for the estimator if not specified during run.
- **seed** (*np.random.Generator |* [*int*](https://docs.python.org/3/library/functions.html#int) *| None*) – The seed or Generator object for random number generation. If None, a random seeded default RNG will be used.

## Attributes

### default\_precision

Return the default precision

### seed

Return the seed or Generator object for random number generation.

## Methods

### run

`run(pubs, *, precision=None)`

[GitHub](https://github.com/Qiskit/qiskit/tree/stable/2.5/qiskit/primitives/statevector_estimator.py#L136-L145)

Estimate expectation values for each provided pub (Primitive Unified Bloc).

**Parameters**

- **pubs** ([*Iterable*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterable)*\[TypeAliasForwardRef('EstimatorPubLike')]*) – An iterable of pub-like objects, such as tuples `(circuit, observables)` or `(circuit, observables, parameter_values)`.
- **precision** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – The target precision for expectation value estimates of each run Estimator Pub that does not specify its own precision. If None the estimator’s default precision value will be used.

**Returns**

A job object that contains results.

**Return type**

[*PrimitiveJob*](/docs/api/qiskit/qiskit.primitives.PrimitiveJob "qiskit.primitives.primitive_job.PrimitiveJob")\[[*PrimitiveResult*](/docs/api/qiskit/qiskit.primitives.PrimitiveResult "qiskit.primitives.containers.primitive_result.PrimitiveResult")\[[*PubResult*](/docs/api/qiskit/qiskit.primitives.PubResult "qiskit.primitives.containers.pub_result.PubResult")]]