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API-doc

.. automodule:: mitiq
   :members:

Benchmarks

Mirror Circuits

.. automodule:: mitiq.benchmarks.mirror_circuits
   :members:

Randomized Benchmarking Circuits

.. automodule:: mitiq.benchmarks.randomized_benchmarking
   :members:

GHZ Circuits

.. automodule:: mitiq.benchmarks.ghz_circuits
   :members:

Quantum Volume Circuits

.. automodule:: mitiq.benchmarks.quantum_volume_circuits
   :members:

Clifford Data Regression

Clifford Data Regression (High-Level Tools)

.. automodule:: mitiq.cdr.cdr
   :members:

Clifford Training Data

.. automodule:: mitiq.cdr.clifford_training_data
   :members:

Data Regression

.. automodule:: mitiq.cdr.data_regression
   :members:

See Ref. :cite:`Czarnik_2021_Quantum` for more details on these methods.

Mitiq - Braket

Conversions

.. automodule:: mitiq.interface.mitiq_braket.conversions
   :members:

Mitiq - Cirq

Cirq Utils

.. automodule:: mitiq.interface.mitiq_cirq.cirq_utils
   :members:

Mitiq - PyQuil

Conversions

.. automodule:: mitiq.interface.mitiq_pyquil.conversions
   :members:

Mitiq - Qiskit

Conversions

.. automodule:: mitiq.interface.mitiq_qiskit.conversions
   :members:

Qiskit Utils

.. automodule:: mitiq.interface.mitiq_qiskit.qiskit_utils
   :members:

Digital Dynamical Decoupling

Digital Dynamical Decoupling (High-Level Tools)

.. automodule:: mitiq.ddd.ddd
   :members:

Insertion

.. automodule:: mitiq.ddd.insertion
   :members:

Rules

.. automodule:: mitiq.ddd.rules.rules
   :members:

Executors

.. automodule:: mitiq.executor.executor
   :members:

Observables

Observable

.. automodule:: mitiq.observable.observable
   :members:

Pauli

.. automodule:: mitiq.observable.pauli
   :members:

Probabilistic Error Cancellation

Probabilistic Error Cancellation (High-Level Tools)

.. automodule:: mitiq.pec.pec
   :members:

Quasi-Probability Representations

.. automodule:: mitiq.pec.representations.optimal
   :members:


.. automodule:: mitiq.pec.representations.damping
   :members:


.. automodule:: mitiq.pec.representations.depolarizing
   :members:

Learning-based PEC

.. automodule:: mitiq.pec.representations.biased_noise
   :members:


.. automodule:: mitiq.pec.representations.learning
   :members:

Sampling from a Noisy Decomposition of an Ideal Operation

.. automodule:: mitiq.pec.sampling
   :members:

Probabilistic Error Cancellation Types

.. automodule:: mitiq.pec.types.types
   :members:

Utilities for Quantum Channels

.. automodule:: mitiq.pec.channels
   :members:

Raw

Run experiments without error mitigation (raw results)

.. automodule:: mitiq.raw.raw

Readout Error Mitigation

Measurement Result

.. autoclass:: mitiq._typing.MeasurementResult
   :members:

Post-selection

.. automodule:: mitiq.rem.post_select
   :members:

Zero Noise Extrapolation

Zero Noise Extrapolation (High-Level Tools)

.. automodule:: mitiq.zne.zne
   :members:

Inference and Extrapolation: Factories

.. automodule:: mitiq.zne.inference
   :members:

Noise Scaling: Unitary Folding

.. automodule:: mitiq.zne.scaling.folding
   :members:

Noise Scaling: Parameter Calibration

.. automodule:: mitiq.zne.scaling.parameter
   :members: