---
title: TREX (latest version)
description: API reference for qiskit_mitigation.TREX in the latest version of qiskit-mitigation
source: https://quantum.cloud.ibm.com/docs/en/api/qiskit-mitigation/trex
---

# TREX (`qiskit_mitigation.trex`)

### TREX

*class* `TREX`

[GitHub](https://github.com/Qiskit/qiskit-mitigation/tree/stable/0.1/qiskit_mitigation/trex.py#L37-L330)

Bases: [`object`](https://docs.python.org/3/builtins/functions.html#object)

Mitigates readout errors in expectation values using the Twirled Readout Error eXtinction (TREX) method.

The class enables adding a readout error calibration item to a QuantumProgram shared by one or more mitigation tasks, that can be executed on hardware using the Executor. The calibration results are post processed into a measurement noise model, which is used to calculate scale factors that correct the expectation values computed by the connected mitigation tasks.

Instantiate a TREX task.

#### Methods

##### prepare

`prepare(num_randomizations, quantum_program, custom_boxing_options=None)`

[GitHub](https://github.com/Qiskit/qiskit-mitigation/tree/stable/0.1/qiskit_mitigation/trex.py#L100-L133)

Adds a TREX calibration item to the quantum program.

**Parameters**

- **num\_randomizations** ([*int*](https://docs.python.org/3/builtins/functions.html#int)) – Number of randomizations for the TREX calibration.
- **quantum\_program** ([*QuantumProgram*](https://qiskit.github.io/samplomatic/api/auto/samplomatic.quantum_program.QuantumProgram.html#samplomatic.quantum_program.QuantumProgram "(in samplomatic)")) – The quantum program to add an item for.
- **custom\_boxing\_options** ([*dict*](https://docs.python.org/3/builtins/stdtypes.html#dict) *| None*) – The custom boxing options that will be used by `generate_boxing_pass_manager()` function.

**Returns**

The input `QuantumProgram` instance with added TREX calibration item.

**Return type**

[*QuantumProgram*](https://qiskit.github.io/samplomatic/api/auto/samplomatic.quantum_program.QuantumProgram.html#samplomatic.quantum_program.QuantumProgram "(in samplomatic)")

##### calculate\_trex\_factor

*static* `calculate_trex_factor(noise_data, observable_term)`

[GitHub](https://github.com/Qiskit/qiskit-mitigation/tree/stable/0.1/qiskit_mitigation/trex.py#L267-L301)

Calculate TREX factor relevant for a given observable term based on noise model.

**Parameters**

- **noise\_data** ([*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)  *|*[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) – PauliLindbladMap containing measurement noise model or a result of TREX calibration execution.
- **observable\_term** ([*Pauli*](/docs/api/qiskit/qiskit.quantum_info.Pauli)  *|*[*QubitSparsePauli*](/docs/api/qiskit/qiskit.quantum_info.QubitSparsePauli)  *|*[*str*](https://docs.python.org/3/builtins/stdtypes.html#str)) – observable term to calculate TREX factor for.

**Returns**

TREX factor for the observable term.

**Return type**

[float](https://docs.python.org/3/builtins/functions.html#float)

##### trex\_factors\_each\_term

*static* `trex_factors_each_term(measurement_noise_map, observables)`

[GitHub](https://github.com/Qiskit/qiskit-mitigation/tree/stable/0.1/qiskit_mitigation/trex.py#L303-L330)

Calculates TREX mitigation algorithm’s expectation value scale factor for each Pauli term in each observable.

Calculates $\langle Z^N \rangle$ for each non-identity Pauli term in each observable using learned measurement noise, where $N$ are the non-identity indices in the term.

**Parameters**

- **measurement\_noise\_map** ([*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)) – Learned measurement noise in PauliLindbladMap format.
- **observables** ([*ObservablesArray*](/docs/api/qiskit/qiskit.primitives.ObservablesArray)  *|*[*Sequence*](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)*\[*[*SparsePauliOp*](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp)*]*) – Observables in which the TREX algorithm mitigates their expectation values.

**Returns**

A dictionary mapping Pauli terms to their expectation values scale factors.

**Return type**

[dict](https://docs.python.org/3/builtins/stdtypes.html#dict)\[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [float](https://docs.python.org/3/builtins/functions.html#float)]

##### compute\_noise\_model

`compute_noise_model(results)`

[GitHub](https://github.com/Qiskit/qiskit-mitigation/tree/stable/0.1/qiskit_mitigation/trex.py#L237-L265)

Compute noise model from program results.

**Parameters**

**results** ([*QuantumProgramResult*](https://qiskit.github.io/samplomatic/api/auto/samplomatic.quantum_program.QuantumProgramResult.html#samplomatic.quantum_program.QuantumProgramResult "(in samplomatic)")) – QuantumProgramResult which contains the TREX calibration circuit results.

**Returns**

The learned readout noise model as a `PauliLindbladMap`.

**Return type**

[*PauliLindbladMap*](/docs/api/qiskit/qiskit.quantum_info.PauliLindbladMap)

##### has\_calibration\_result

`has_calibration_result()`

[GitHub](https://github.com/Qiskit/qiskit-mitigation/tree/stable/0.1/qiskit_mitigation/trex.py#L135-L137)

Whether a TREX calibration task has been added to the quantum program.

**Return type**

[bool](https://docs.python.org/3/builtins/functions.html#bool)