122 lines
5.3 KiB
Plaintext
122 lines
5.3 KiB
Plaintext
---
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title: StatevectorEstimator
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description: API reference for qiskit.primitives.StatevectorEstimator
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.primitives.StatevectorEstimator
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---
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# StatevectorEstimator
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<Class id="qiskit.primitives.StatevectorEstimator" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit/tree/stable/1.1/qiskit/primitives/statevector_estimator.py#L31-L165" signature="qiskit.primitives.StatevectorEstimator(*, default_precision=0.0, seed=None)" modifiers="class">
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Bases: [`BaseEstimatorV2`](qiskit.primitives.BaseEstimatorV2 "qiskit.primitives.base.base_estimator.BaseEstimatorV2")
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Simple implementation of [`BaseEstimatorV2`](qiskit.primitives.BaseEstimatorV2 "qiskit.primitives.BaseEstimatorV2") with full state vector simulation.
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This class is implemented via [`Statevector`](qiskit.quantum_info.Statevector "qiskit.quantum_info.Statevector") which turns provided circuits into pure state vectors. These states are subsequently acted on by :class:\~.SparsePauliOp\`, which implies that, at present, this implementation is only compatible with Pauli-based observables.
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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()` method can be given a sequence of pubs to run in one call.
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```python
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from qiskit.circuit import Parameter, QuantumCircuit
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from qiskit.primitives import StatevectorEstimator
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from qiskit.quantum_info import Pauli, SparsePauliOp
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import matplotlib.pyplot as plt
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import numpy as np
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# Define a circuit with two parameters.
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circuit = QuantumCircuit(2)
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circuit.h(0)
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circuit.cx(0, 1)
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circuit.ry(Parameter("a"), 0)
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circuit.rz(Parameter("b"), 0)
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circuit.cx(0, 1)
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circuit.h(0)
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# Define a sweep over parameter values, where the second axis is over
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# the two parameters in the circuit.
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params = np.vstack([
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np.linspace(-np.pi, np.pi, 100),
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np.linspace(-4 * np.pi, 4 * np.pi, 100)
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]).T
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# Define three observables. Many formats are supported here including
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# classes such as qiskit.quantum_info.SparsePauliOp. The inner length-1
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# lists cause this array of observables to have shape (3, 1), rather
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# than shape (3,) if they were omitted.
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observables = [
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[SparsePauliOp(["XX", "IY"], [0.5, 0.5])],
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[Pauli("XX")],
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[Pauli("IY")]
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]
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# Instantiate a new statevector simulation based estimator object.
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estimator = StatevectorEstimator()
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# Estimate the expectation value for all 300 combinations of
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# observables and parameter values, where the pub result will have
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# shape (3, 100). This shape is due to our array of parameter
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# bindings having shape (100,), combined with our array of observables
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# having shape (3, 1)
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pub = (circuit, observables, params)
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job = estimator.run([pub])
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# Extract the result for the 0th pub (this example only has one pub).
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result = job.result()[0]
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# Error-bar information is also available, but the error is 0
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# for this StatevectorEstimator.
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result.data.stds
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# Pull out the array-based expectation value estimate data from the
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# result and plot a trace for each observable.
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for idx, pauli in enumerate(observables):
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plt.plot(result.data.evs[idx], label=pauli)
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plt.legend()
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```
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**Parameters**
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* **default\_precision** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – The default precision for the estimator if not specified during run.
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* **seed** (*np.random.Generator |* [*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)") *| None*) – The seed or Generator object for random number generation. If None, a random seeded default RNG will be used.
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## Attributes
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### default\_precision
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<Attribute id="qiskit.primitives.StatevectorEstimator.default_precision">
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Return the default precision
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</Attribute>
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### seed
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<Attribute id="qiskit.primitives.StatevectorEstimator.seed">
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Return the seed or Generator object for random number generation.
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</Attribute>
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## Methods
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### run
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<Function id="qiskit.primitives.StatevectorEstimator.run" github="https://github.com/Qiskit/qiskit/tree/stable/1.1/qiskit/primitives/statevector_estimator.py#L127-L136" signature="run(pubs, *, precision=None)">
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Estimate expectation values for each provided pub (Primitive Unified Bloc).
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**Parameters**
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* **pubs** (*Iterable\[EstimatorPubLike]*) – An iterable of pub-like objects, such as tuples `(circuit, observables)` or `(circuit, observables, parameter_values)`.
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* **precision** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)") *| 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.
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**Returns**
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A job object that contains results.
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**Return type**
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[PrimitiveJob](qiskit.primitives.PrimitiveJob "qiskit.primitives.PrimitiveJob")\[[PrimitiveResult](qiskit.primitives.PrimitiveResult "qiskit.primitives.PrimitiveResult")\[[PubResult](qiskit.primitives.PubResult "qiskit.primitives.PubResult")]]
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</Function>
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</Class>
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