---
title: Estimator quickstart
description: A quickstart guide on how to use the IBM Quantum Estimator primitive.
source: https://eu-de.quantum.cloud.ibm.com/docs/en/guides/get-started-with-estimator
---

# Estimator quickstart

The Estimator primitive computes the expectation values for one or more observables with respect to states prepared by quantum circuits. The circuits can be parametrized, as long as the parameter values are also provided as input to the primitive.

This primitive has several built-in [error mitigation and suppression techniques](/docs/guides/error-mitigation-and-suppression-techniques) techniques, including dynamical decoupling, Pauli-twirling, gate-folding ZNE, PEA, and PEC. It also supports a `resilience_level` option that allows you to easily configure the cost and accuracy tradeoff.

The steps in this topic describe how to set up Estimator, explore the options you can use to configure it, and invoke it in a program.

### Package versions

The code on this page was developed using the following requirements.
We recommend using these versions or newer.

```
qiskit[all]~=2.5.1
qiskit-ibm-runtime~=0.47.0
```

## Steps to use the Estimator primitive

### 1. Initialize the account

You first need to initialize your account. You can then select the QPU you want to use to calculate the expectation value.

Follow the steps in the [Set up your IBM Cloud account](/docs/guides/cloud-setup) if you don't already have an account.

> **Fractional gates**
>
> To use the newly supported [fractional gates](/docs/guides/fractional-gates), set `use_fractional_gates=True` when requesting a backend from a `QiskitRuntimeService` instance. For example:
>
> ```python
> service = QiskitRuntimeService()
> fractional_gate_backend = service.least_busy(use_fractional_gates=True)
> ```
>
> This is an experimental feature and might change in the future.

```python
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()
backend = service.least_busy(
    operational=True, simulator=False, min_num_qubits=127
)

print(backend.name)
```

Output:

```
ibm_fez
```

### 2. Create a circuit and an observable

You need at least one circuit and one observable as inputs to the Estimator primitive.

```python
from qiskit.circuit.library import qaoa_ansatz
from qiskit.quantum_info import SparsePauliOp

entanglement = [tuple(edge) for edge in backend.coupling_map.get_edges()]
observable = SparsePauliOp.from_sparse_list(
    [("ZZ", [i, j], 0.5) for i, j in entanglement],
    num_qubits=backend.num_qubits,
)
circuit = qaoa_ansatz(observable, reps=2)
# The circuit is parametrized, so we will define the parameter values for execution
param_values = [0.1, 0.2, 0.3, 0.4]
```

The circuit and observable need to be transformed to only use instructions supported by the QPU (referred to as *instruction set architecture (ISA)* circuits). Use the transpiler to do this.

```python
from qiskit.transpiler import generate_preset_pass_manager

pm = generate_preset_pass_manager(optimization_level=1, backend=backend)
isa_circuit = pm.run(circuit)
isa_observable = observable.apply_layout(isa_circuit.layout)
print(f">>> Circuit ops (ISA): {isa_circuit.count_ops()}")
```

Output:

```
>>> Circuit ops (ISA): OrderedDict([('rz', 4472), ('sx', 1884), ('cz', 1120)])
```

### 3. Initialize the IBM Quantum Estimator

When you initialize Estimator, use the `mode` parameter to specify the mode you want it to run in.  Possible values are `batch`, `session`, or `backend` objects for batch, session, and job execution mode, respectively. For more information, see [Introduction to IBM Quantum Compute Service execution modes.](/docs/guides/execution-modes) Note that Open Plan users cannot submit session jobs.

```python
from qiskit_ibm_runtime import EstimatorV2 as Estimator

estimator = Estimator(mode=backend)
```

### 4. Invoke Estimator and get results

Next, invoke the `run()` method to calculate expectation values for the input circuits and observables. The circuit, observable, and optional parameter value sets are input as *primitive unified bloc* (PUB) tuples.

```python
job = estimator.run([(isa_circuit, isa_observable, param_values)])
print(f">>> Job ID: {job.job_id()}")
print(f">>> Job Status: {job.status()}")
```

Output:

```
>>> Job ID: d9mq9tnurbec73e67aj0
>>> Job Status: QUEUED
```

```python
result = job.result()
print(f">>> {result}")
print(f"  > Expectation value: {result[0].data.evs}")
print(f"  > Metadata: {result[0].metadata}")
```

Output:

```
>>> PrimitiveResult([PubResult(data=DataBin(evs=np.ndarray(<shape=(), dtype=float64>), stds=np.ndarray(<shape=(), dtype=float64>), ensemble_standard_error=np.ndarray(<shape=(), dtype=float64>)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32})], metadata={'dynamical_decoupling': {'enable': False, 'sequence_type': 'XX', 'extra_slack_distribution': 'middle', 'scheduling_method': 'alap'}, 'twirling': {'enable_gates': False, 'enable_measure': True, 'num_randomizations': 'auto', 'shots_per_randomization': 'auto', 'interleave_randomizations': True, 'strategy': 'active-accum'}, 'resilience': {'measure_mitigation': True, 'zne_mitigation': False, 'pec_mitigation': False}, 'version': 2})
  > Expectation value: 29.745702323497138
  > Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}
```

## Next steps

> **Recommendations**
>
> - Learn how to [test locally](/docs/guides/local-testing-mode) before running on quantum computers.
> - Review detailed [examples](/docs/guides/estimator-examples).
> - Practice with primitives by working through the [Cost function lesson](/learning/courses/variational-algorithm-design/cost-functions) in IBM Quantum Learning.
> - Learn how to transpile locally in the [Transpile](/docs/guides/transpile/) section.
> - Try the [Compare transpiler settings](/docs/guides/circuit-transpilation-settings#compare-transpiler-settings) guide.
> - Learn how to [use the primitive options](/docs/guides/runtime-options-overview).
> - View the API for [Estimator](/docs/api/qiskit-ibm-runtime/options-estimator-options) options.