The Quantum Information Software Kit (QISKit for short) is a software development kit (SDK) for working with OpenQASM and the IBM Q experience (QX).

Use QISKit to create quantum computing programs, compile them, and execute them on one of several backends (online Real quantum processors, online simulators, and local simulators). For the online backends, QISKit uses our python API client to connect to the IBM Q experience.

We use GitHub issues for tracking requests and bugs. Please see the IBM Q experience community for questions and discussion.

If you'd like to contribute to QISKit, please take a look at our contribution guidelines.

Links to Sections:

• Installation
• Creating your first Quantum Program
• More Information
• Authors
• License

At least Python 3.5 or later is needed for using QISKit. In addition, Jupyter Notebook is recommended for interacting with the tutorials. For this reason we recommend installing the Anaconda 3 python distribution, as it comes with all of these dependencies pre-installed.

In addition, a basic understanding of quantum information is very helpful when interacting with QISKit. If you're new to quantum, start with our User Guides!

We encourage to install QISKit via the PIP tool (a python package manager):

    pip install qiskit

PIP will handle all dependencies automatically for us and you will always install the latest (and well-tested) version.

PIP package comes with prebuilt binaries for these platforms:

• Linux x86_64
• Darwin
• Win64

If your platform is not in the list, PIP will try to build from the sources at installation time. It will require to have CMake 3.5 or higher pre-installed and at least one of the build environments supported by CMake.

If during the installation PIP doesn't succeed to build, don't worry, you will have QISKit installed at the end but you probably couldn't take advantage of some of the high-performance components. Anyway, we always provide a python, not-so-fast alternative as a fallback.

We recommend using python virtual environments to improve your experience. Refer to our Environment Setup documentation for more information.

Now that the SDK is installed, it's time to begin working with QISKit.

We are ready to try out a quantum circuit example, which runs via the local simulator.

This is a simple example that makes an entangled state.

from qiskit import QuantumProgram, QISKitError, RegisterSizeError

# Create a QuantumProgram object instance.
q_program = QuantumProgram()
backend = 'local_qasm_simulator'
try:
    # Create a Quantum Register called "qr" with 2 qubits.
    quantum_reg = q_program.create_quantum_register("qr", 2)
    # Create a Classical Register called "cr" with 2 bits.
    classical_reg = q_program.create_classical_register("cr", 2)
    # Create a Quantum Circuit called "qc" involving the Quantum Register "qr"
    # and the Classical Register "cr".
    quantum_circuit =
        q_program.create_circuit("bell", [quantum_reg],[classical_reg])

    # Add the H gate in the Qubit 0, putting this qubit in superposition.
    quantum_circuit.h(quantum_reg[0])
    # Add the CX gate on control qubit 0 and target qubit 1, putting
    # the qubits in a Bell state
    quantum_circuit.cx(quantum_reg[0], quantum_reg[1])

    # Add a Measure gate to see the state.
    quantum_circuit.measure(quantum_reg, classical_reg)

    # Compile and execute the Quantum Program in the local_qasm_simulator.
    result = q_program.execute(["bell"], backend=backend, shots=1024, seed=1)

    # Show the results.
    print(result)
    print(result.get_data("bell"))

except QISKitError as ex:
    print('There was an error in the circuit!. Error = {}'.format(ex))
except RegisterSizeError as ex:
    print('Error in the number of registers!. Error = {}'.format(ex))

In this case, the output will be:

COMPLETED
{'counts': {'00': 512, '11': 512}}

This script is avaiable here.

You can also use QISKit to execute your code on a real Quantum Chip. In order to do so, you need to configure the SDK for using the credentials for your Quantum Experience Account:

1. Create an IBM Q experience> account if you haven't already done so
2. Get an API token from the IBM Q experience website under "My Account" > "Personal Access Token". This API token allows you to execute your programs with the IBM Q experience backends. Example.
3. We are going to create a new file called Qconfig.py and insert the API token into it. This file must have these contents:

APItoken = 'MY_API_TOKEN'

config = {
    'url': 'https://quantumexperience.ng.bluemix.net/api',
    # The following should only be needed for IBM Q users.
    'hub': 'MY_HUB',
    'group': 'MY_GROUP',
    'project': 'MY_PROJECT'
}

4. Substitute 'MY_API_TOKEN' with your real API Token extracted in step 2.

5. If you have access to the IBM Q features, you also need to setup the values for your hub, group, and project. You can do so by filling the config variable with the values you can find on your IBM Q account page.

Once the Qconfig.py file is set up, you have to move it under the same directory/folder where your program/tutorial resides, so it can be imported and be used to authenticate with QuantumProgram.set_api() function. For example:

from qiskit import QuantumProgram
import Qconfig

# Creating Programs create your first QuantumProgram object instance.
Q_program = QuantumProgram()
Q_program.set_api(Qconfig.APItoken, Qconfig.config["url"], verify=False,
                  hub=Qconfig.config["hub"],
                  group=Qconfig.config["group"],
                  project=Qconfig.config["project"])

For more details on this and more information see our QISKit documentation.

Now you're set up and ready to check out some of the other examples from our Tutorial repository. Start with the index tutorial and then go to the ‘Getting Started’ example. If you already have Jupyter Notebooks installed, you can copy and modify the notebooks to create your own experiments.

To install the tutorials as part of the QISKit SDK, see the following installation details. Complete SDK documentation can be found in the doc directory and in the official QISKit site.

For more information on how to use QISKit, tutorial examples, and other helpful links, take a look at these resources:

• User Guides, a good starting place for learning about quantum information and computing
• Tutorials, for example notebooks, start with the index and ‘Getting Started’ Jupyter notebook
• OpenQASM, for additional information and examples of QASM code
• IBM Quantum Experience Composer, a GUI for interacting with real and simulated quantum computers
• QISkit Python API, an API to use the IBM Quantum Experience in Python

QISKit was originally developed by researchers and developers on the IBM-Q Team at IBM Research, with the aim of offering a high level development kit to work with quantum computers.

Visit the IBM Q experience community for questions and discussions on QISKit and quantum computing more broadly. If you'd like to contribute to QISKit, please take a look at our contribution guidelines.

• Korean Translation, Basic guide line written in Korean.
• Chinese Translation, Basic guide line written in Chinese.

Ismail Yunus Akhalwaya, Jim Challenger, Andrew Cross, Vincent Dwyer, Mark Everitt, Ismael Faro, Jay Gambetta, Juan Gomez, Yunho Maeng, Paco Martin, Antonio Mezzacapo, Diego Moreda, Jesus Perez, Russell Rundle, Todd Tilma, John Smolin, Erick Winston, Chris Wood.

In future releases, anyone who contributes with code to this project is welcome to include their name here.

This project uses the Apache License Version 2.0 software license.