mitiq 0.27.0

Mitiq is an open source toolkit for implementing error mitigation techniques on most current intermediate-scale quantum computers.

Mitiq is a Python toolkit for implementing error mitigation techniques on quantum computers.

Current quantum computers are noisy due to interactions with the environment, imperfect gate applications, state preparation and measurement errors, etc. Error mitigation seeks to reduce these effects at the software level by compiling quantum programs in clever ways.

Want to know more? Check out our documentation and chat with us on Discord.

Quickstart

Installation

pip install mitiq

Example

Define a function which inputs a circuit and returns an expectation value you want to compute, then use Mitiq to mitigate errors.

import cirq
from mitiq import zne, benchmarks


def execute(circuit, noise_level=0.005):
    """Returns Tr[ρ |0⟩⟨0|] where ρ is the state prepared by the circuit
    with depolarizing noise."""
    noisy_circuit = circuit.with_noise(cirq.depolarize(p=noise_level))
    return (
        cirq.DensityMatrixSimulator()
        .simulate(noisy_circuit)
        .final_density_matrix[0, 0]
        .real
    )


circuit = benchmarks.generate_rb_circuits(n_qubits=1, num_cliffords=50)[0]

true_value = execute(circuit, noise_level=0.0)      # Ideal quantum computer
noisy_value = execute(circuit)                      # Noisy quantum computer
zne_value = zne.execute_with_zne(circuit, execute)  # Noisy quantum computer + Mitiq

print(f"Error w/o  Mitiq: {abs((true_value - noisy_value) / true_value):.3f}")
print(f"Error w Mitiq:    {abs((true_value - zne_value) / true_value):.3f}")

Sample output:

Error w/o  Mitiq: 0.264
Error w Mitiq:    0.073

Calibration

Unsure which error mitigation technique or parameters to use? Try out the calibration module demonstrated below to help find the best parameters for you particular backend!

See our guides and examples for more explanation, techniques, and benchmarks. The examples and other notebooks can be run interactively on the cloud with mybinder.org.

Quick Tour

Error mitigation techniques

Technique	Documentation	Mitiq module	Paper Reference(s)
Zero-noise extrapolation	ZNE	mitiq.zne	1611.09301 1612.02058 1805.04492
Probabilistic error cancellation	PEC	mitiq.pec	1612.02058 1712.09271 1905.10135
(Variable-noise) Clifford data regression	CDR	mitiq.cdr	2005.10189 2011.01157
Digital dynamical decoupling	DDD	mitiq.ddd	9803057 1807.08768
Readout-error mitigation	REM	mitiq.rem	1907.08518 2006.14044

See our roadmap for additional candidate techniques to implement. If there is a technique you are looking for, please file a feature request.

Interface

We refer to any programming language you can write quantum circuits in as a frontend, and any quantum computer / simulator you can simulate circuits in as a backend.

Supported frontends

Cirq	Qiskit	pyQuil	Braket	PennyLane

Note: Cirq is a core requirement of Mitiq and is installed when you pip install mitiq.

Supported backends

You can use Mitiq with any backend you have access to that can interface with supported frontends.

Benchmarks

Mitiq uses asv to benchmark the core functionalities of the project. They are found in the benchmarks/ directory and their changes can be seen overtime at https://benchmarks.mitiq.dev/.

Citing Mitiq

If you use Mitiq in your research, please reference the Mitiq whitepaper using the bibtex entry found in CITATION.bib.

A list of papers citing Mitiq can be found on Google Scholar / Semantic Scholar.

License

GNU GPL v.3.0.

Contributing

We welcome contributions to Mitiq including bug fixes, feature requests, etc. To get started, check out our contribution guidelines and/or documentation guidelines.

Contributors ✨

Thank you to all of the wonderful people that have made this project possible. Non-code contributors are also much appreciated, and are listed here. Thank you to

• @francespoblete for much of Mitiq's design work/vision

Contributions of any kind are welcome!