Cirq

A Python framework for creating, editing, and invoking Noisy Intermediate-Scale Quantum (NISQ) circuits.

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Quick Overview

Cirq is an open-source framework for creating, manipulating, and optimizing quantum circuits and running them on quantum computers and simulators. Developed by Google's Quantum AI team, Cirq provides a Python library for writing, manipulating, and optimizing quantum algorithms.

Pros

Flexible and extensible architecture for quantum circuit design
Strong integration with Google's quantum hardware and simulators
Comprehensive set of tools for quantum circuit optimization and error mitigation
Active community and regular updates from Google's Quantum AI team

Cons

Steeper learning curve compared to some other quantum computing frameworks
Limited support for non-Google quantum hardware
Documentation can be complex for beginners in quantum computing
Some advanced features may require in-depth knowledge of quantum mechanics

Code Examples

Creating a simple quantum circuit:

import cirq

# Create two qubits
q0, q1 = cirq.LineQubit.range(2)

# Create a circuit
circuit = cirq.Circuit(
    cirq.H(q0),  # Hadamard gate on q0
    cirq.CNOT(q0, q1),  # CNOT gate with q0 as control and q1 as target
    cirq.measure(q0, q1, key='result')  # Measure both qubits
)

print(circuit)

Running a simulation:

import cirq

# Create a circuit (using the previous example)
# ...

# Create a simulator
simulator = cirq.Simulator()

# Run the simulation
result = simulator.run(circuit, repetitions=1000)

# Print the results
print(result.histogram(key='result'))

Using a noise model:

import cirq

# Create a circuit (using the previous example)
# ...

# Define a noise model
noise_model = cirq.depolarize(p=0.1)

# Create a noisy simulator
noisy_simulator = cirq.DensityMatrixSimulator(noise=noise_model)

# Run the noisy simulation
noisy_result = noisy_simulator.run(circuit, repetitions=1000)

print(noisy_result.histogram(key='result'))

Getting Started

To get started with Cirq:

Install Cirq using pip:
```
pip install cirq
```
Import Cirq in your Python script:
```
import cirq
```

Create a simple quantum circuit:

q0 = cirq.LineQubit(0)
circuit = cirq.Circuit(
    cirq.H(q0),
    cirq.measure(q0, key='result')
)

Run a simulation:

simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=100)
print(result.histogram(key='result'))

Competitor Comparisons

qiskit

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Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.

Pros of Qiskit

Extensive documentation and tutorials for beginners
Strong integration with IBM Quantum hardware and cloud services
Comprehensive suite of tools for quantum chemistry and finance applications

Cons of Qiskit

Steeper learning curve for users new to quantum computing
Less flexibility in low-level circuit manipulation compared to Cirq

Code Comparison

Qiskit:

from qiskit import QuantumCircuit, execute, Aer

qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])

backend = Aer.get_backend('qasm_simulator')
job = execute(qc, backend, shots=1000)
result = job.result()

Cirq:

import cirq

q0, q1 = cirq.LineQubit.range(2)
circuit = cirq.Circuit(
    cirq.H(q0),
    cirq.CNOT(q0, q1),
    cirq.measure(q0, q1, key='m')
)
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1000)

Both Qiskit and Cirq are powerful quantum computing frameworks, each with its own strengths. Qiskit excels in its integration with IBM's quantum hardware and provides extensive resources for various quantum applications. Cirq offers more flexibility for low-level circuit manipulation and is particularly suited for researchers and developers working on custom quantum algorithms.

pennylane

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PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

Pros of PennyLane

Supports a wider range of quantum hardware and software platforms
Offers automatic differentiation for quantum-classical hybrid computations
Provides a more extensive library of built-in quantum operations and templates

Cons of PennyLane

Steeper learning curve for beginners due to its more comprehensive feature set
Less focus on low-level circuit manipulation compared to Cirq
Smaller community and fewer educational resources available

Code Comparison

PennyLane:

import pennylane as qml

dev = qml.device('default.qubit', wires=2)

@qml.qnode(dev)
def circuit(params):
    qml.RX(params[0], wires=0)
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(1))

Cirq:

import cirq

q0, q1 = cirq.LineQubit.range(2)

def circuit(params):
    return cirq.Circuit(
        cirq.rx(params[0])(q0),
        cirq.CNOT(q0, q1),
        cirq.measure(q1, key='result')
    )

Both examples demonstrate a simple quantum circuit with a parameterized rotation and a CNOT gate. PennyLane uses a decorator-based approach and built-in expectation value calculation, while Cirq focuses on explicit circuit construction and measurement.

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A Python library for quantum programming using Quil.

Pros of pyquil

Tighter integration with Rigetti's quantum hardware and cloud services
More extensive support for quantum-classical hybrid algorithms
Robust simulation capabilities, including noise models

Cons of pyquil

Steeper learning curve for beginners
Less extensive documentation compared to Cirq
Primarily focused on Rigetti's ecosystem, potentially limiting flexibility

Code Comparison

pyquil:

from pyquil import Program
from pyquil.gates import H, CNOT

p = Program()
p += H(0)
p += CNOT(0, 1)

Cirq:

import cirq

q0, q1 = cirq.LineQubit.range(2)
circuit = cirq.Circuit(
    cirq.H(q0),
    cirq.CNOT(q0, q1)
)

Both examples create a simple quantum circuit with a Hadamard gate followed by a CNOT gate. pyquil uses a more imperative style, while Cirq adopts a more declarative approach. Cirq's syntax may be more intuitive for those familiar with Python's object-oriented programming.

Quantum

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Pros of Quantum

More comprehensive development environment with Q# language and Visual Studio integration
Extensive documentation and learning resources, including tutorials and samples
Strong support for quantum algorithm development and quantum chemistry simulations

Cons of Quantum

Steeper learning curve due to Q# language specificity
Less flexibility for low-level circuit manipulation compared to Cirq
More focused on Microsoft's quantum hardware and simulators

Code Comparison

Quantum (Q#):

operation BellPair(q1 : Qubit, q2 : Qubit) : Unit {
    H(q1);
    CNOT(q1, q2);
}

Cirq:

def bell_pair(q1, q2):
    yield cirq.H(q1)
    yield cirq.CNOT(q1, q2)

Both examples create a Bell pair, but Quantum uses Q# with its specific syntax, while Cirq uses Python with a more familiar approach for many developers. Cirq's implementation is more concise and may be easier for those with Python experience to understand quickly. However, Quantum's Q# offers more quantum-specific features and optimizations that may be beneficial for complex quantum algorithms.

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QuTiP: Quantum Toolbox in Python

Pros of QuTiP

More comprehensive for open quantum systems and master equation solvers
Extensive library of pre-built quantum objects and operations
Strong support for visualization and data analysis of quantum systems

Cons of QuTiP

Steeper learning curve for beginners in quantum computing
Less focus on quantum circuit design and implementation
Slower execution speed for large-scale quantum simulations

Code Comparison

QuTiP example:

import qutip as qt

q = qt.Qobj([[1], [0]])  # Create a qubit in state |0⟩
H = qt.sigmax()  # Hadamard gate
result = H * q  # Apply Hadamard gate to qubit

Cirq example:

import cirq

q = cirq.NamedQubit('q')  # Create a qubit
circuit = cirq.Circuit(cirq.H(q))  # Apply Hadamard gate
result = cirq.Simulator().simulate(circuit)

Both libraries offer quantum computing capabilities, but QuTiP is more focused on open quantum systems and advanced quantum mechanics, while Cirq is tailored for quantum circuit design and near-term quantum algorithms. QuTiP provides a richer set of pre-built quantum objects and operations, making it powerful for theoretical quantum physics. Cirq, on the other hand, offers a more intuitive approach to building quantum circuits and is better suited for practical quantum algorithm implementation.

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README

Python package for writing, manipulating, and running quantum circuits on quantum computers and simulators.

Features – Installation – Quick Start – Documentation – Integrations – Community – Citing Cirq – Contact

Features

Cirq provides useful abstractions for dealing with todayâs noisy intermediate-scale quantum (NISQ) computers, where the details of quantum hardware are vital to achieving state-of-the-art results. Some of its features include:

Flexible gate definitions and custom gates
Parameterized circuits with symbolic variables
Circuit transformation, compilation and optimization
Hardware device modeling
Noise modeling
Multiple built-in quantum circuit simulators
Integration with qsim for high-performance simulation
Interoperability with NumPy and SciPy
Cross-platform compatibility

Installation

Cirq supports Python version 3.10 and later, and can be used on Linux, MacOS, and Windows, as well as Google Colab. For complete installation instructions, please refer to the Install section of the online Cirq documentation.

Quick Start â âHello Qubitâ Example

Here is a simple example to get you up and running with Cirq after you have installed it. Start a Python interpreter, and then type the following:

import cirq

# Pick a qubit.
qubit = cirq.GridQubit(0, 0)

# Create a circuit.
circuit = cirq.Circuit(
    cirq.X(qubit)**0.5,  # Square root of NOT.
    cirq.measure(qubit, key='m')  # Measurement.
)
print("Circuit:")
print(circuit)

# Simulate the circuit several times.
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=20)
print("Results:")
print(result)

Python should then print output similar to this:

Circuit:
(0, 0): âââX^0.5âââM('m')âââ
Results:
m=11000111111011001000

Congratulations! You have run your first quantum simulation in Cirq. You can continue to learn more by exploring the many Cirq tutorials described below.

Cirq Documentation

The primary documentation site for Cirq is the Cirq home page on the Quantum AI website. There and elsewhere, a variety of documentation for Cirq is available.

Tutorials

Video tutorials on YouTube are an engaging way to learn Cirq.
Jupyter notebook-based tutorials let you learn Cirq from your browser â no installation needed.
Text-based tutorials on the Cirq home page are great when combined with a local installation of Cirq on your computer. After starting with the basics, you'll be ready to dive into tutorials on circuit building and circuit simulation under the Build and Simulate tabs, respectively. Check out the other tabs for more!

Reference Documentation

Docs for the current stable release correspond to what you get with pip install cirq.
Docs for the pre-release correspond to what you get with pip install cirq~=1.0.dev.

Examples

The examples subdirectory of the Cirq GitHub repo has many programs illustrating the application of Cirq to everything from common textbook algorithms to more advanced methods.
The Experiments page on the Cirq documentation site has yet more examples, from simple to advanced.

Change log

The Cirq releases page on GitHub lists the changes in each release.

Integrations

Google Quantum AI has a suite of open-source software that lets you do more with Cirq. From high-performance simulators, to novel tools for expressing and analyzing fault-tolerant quantum algorithms, our software stack lets you develop quantum programs for a variety of applications.

Your interests	Software to explore
Quantum algorithms? Fault-tolerant quantum computing (FTQC)?	Qualtran
Large circuits and/or a lot of simulations?	qsim
Circuits with thousands of qubits and millions of Clifford operations?	Stim
Quantum error correction (QEC)?	Stim
Chemistry and/or material science?	OpenFermion OpenFermion-FQE OpenFermion-PySCF OpenFermion-Psi4
Quantum machine learning (QML)?	TensorFlow Quantum
Real experiments using Cirq?	ReCirq

Community

Cirq has benefited from open-source contributions by over 200 people and counting. We are dedicated to cultivating an open and inclusive community to build software for quantum computers, and have a code of conduct for our community.

Announcements

Stay on top of Cirq developments using the approach that best suits your needs:

For releases and major announcements: sign up to the low-volume mailing list cirq-announce.
For releases only:
- Via GitHub notifications: configure repository notifications for Cirq.
- Via Atom/RSS from GitHub: subscribe to the GitHub Cirq releases Atom feed.
- Via RSS from PyPI: subscribe to the PyPI releases RSS feed for Cirq.

Cirq releases take place approximately every quarter.

Questions and Discussions

Do you have questions about using Cirq? Post them to the Quantum Computing Stack Exchange and tag them with the cirq tag. You can also search past questions using that tag â it's a great way to learn!
Would you like to get more involved in Cirq development? Cirq Cynq is our biweekly virtual meeting of contributors to discuss everything from issues to ongoing efforts, as well as to ask questions. Become a member of cirq-dev to get an automatic meeting invitation!

Issues and Pull Requests

Do you have a feature request or want to report a bug? Open an issue on GitHub to report it!
Do you have a code contribution? Read our contribution guidelines, then open a pull request!

Citing Cirq

When publishing articles or otherwise writing about Cirq, please cite the Cirq version you use â it will help others reproduce your results. We use Zenodo to preserve releases. The following links let you download the bibliographic record for the latest stable release of Cirq in some popular formats:

$Download BibTeX bibliography record for latest Cirq release$

For formatted citations and records in other formats, as well as records for all releases of Cirq past and present, please visit the Cirq page on Zenodo.

Contact

For any questions or concerns not addressed here, please email quantum-oss-maintainers@google.com.

Disclaimer

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of Qiskit

Cons of Qiskit

Code Comparison

Pros of PennyLane

Cons of PennyLane

Code Comparison

Pros of pyquil

Cons of pyquil

Code Comparison

Pros of Quantum

Cons of Quantum

Code Comparison

Pros of QuTiP

Cons of QuTiP

Code Comparison

Convert designs to code with AI

README

Features

Installation

Quick Start â âHello Qubitâ Example

Cirq Documentation

Tutorials

Reference Documentation

Examples

Change log

Integrations

Community

Announcements

Questions and Discussions

Issues and Pull Requests

Citing Cirq

Contact

Disclaimer

Top Related Projects

Convert designs to code with AI

Quick Start â âHello Qubitâ Example