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🎉 Starting on November 29, 2023 Qiskit Documentation and Learning will only be
live on IBM QuantumCheck it out

[kiss-kit] noun, software

--------------------------------------------------------------------------------

 1. open-source toolkit for useful quantum computing.
 2. production-ready circuit compiler.

Get started
qiskit 0.45.0
see release notes

# Build a circuit

from qiskit import QuantumCircuit

circuit = QuantumCircuit(2, 2)

circuit.h(0)

circuit.cx(0,1)

circuit.measure([0,1], [0,1])

 

# Connect to your quantum provider

from <quantum provider> import Sampler

sampler = Sampler()

 

# Run the circuit and get the result

job = sampler.run(circuit)

quasi_dist = job.result().quasi_dists[0]

print(quasi_dist)


WHAT CAN QISKIT DO


CIRCUIT LIBRARY

Qiskit includes a comprehensive set of quantum gates and a variety of pre-built
circuits so users at all levels can use Qiskit for research and application
development.

View the circuit library


TRANSPILER

The transpiler translates Qiskit code into an optimized circuit using a
backend’s native gate set, allowing users to program for any quantum processor.
Users can transpile with Qiskit's default optimization, use a custom
configuration or develop their own plugin.

Read about transpilation


RUN ON ANY HARDWARE

Qiskit helps users schedule and run quantum programs on a variety of local
simulators and cloud-based quantum processors. It supports several quantum
hardware designs, such as superconducting qubits and trapped ions.

See all providers


TRY IT YOURSELF

Ready to explore Qiskit’s capabilities for yourself? Learn how to run Qiskit in
the cloud or your local Python environment.

Get started here
QISKIT DOCUMENTATION AND LEARNING RESOURCES ARE MOVING TO IBM QUANTUM ON NOV 29,
2023. LEARN MORE →


DOCUMENTATION

We have reorganized Qiskit documentation on IBM Quantum Platform to better
support your research and development workflows.

Check out Documentation


LEARNING

We have built a new learning application with courses and tutorials to help you
learn the basics and start experimenting with Qiskit.

Check out Learning



BACKEND COMPATIBILITY

You can use Qiskit to construct quantum programs and run them on simulators or
real quantum computers. With our extensive network of providers you can compile
your Qiskit code for a huge range of different backends, more than any other
quantum framework!

Select a provider below and explore code examples of how to use it with Qiskit:

   
   
 * Qiskit (with built-in simulator) View docs
   
   
 * IBM Quantum View docs
   
   
 * Aer View docs
   
   
 * Amazon Braket View docs
   
   
 * AQT View docs
   
   
 * Azure Quantum View docs
   
   
 * Gaqqie View docs
   
   
 * IonQ View docs
   
   
 * IQM View docs
   
   
 * MQT DDSIM View docs
   
   
 * NVIDIA cuStateVec View docs
   
   
 * QC Ware Forge View docs
   
   
 * QuaC View docs
   
   
 * Quantinuum View docs
   
   
 * Rigetti View docs
   
   
 * Strangeworks View docs
   
   
   
   
   

See all providers


INSTALL

pip install qiskit

Copy


BUILD AND RUN

TranspileSample a Bell StateRun VQE

# Build circuit

from qiskit.circuit.library import QuantumVolume

circuit = QuantumVolume(5)


# Transpile circuit

from qiskit import transpile

transpiled_circuit = transpile(circuit, basis_gates=['sx', 'rz', 'cx'])

transpiled_circuit.draw()

Copy

from qiskit.primitives import Sampler

sampler = Sampler()


# Build circuit

from qiskit import QuantumCircuit

circuit = QuantumCircuit(2, 2)

circuit.h(0)

circuit.cx(0,1)

circuit.measure([0,1], [0,1])


# Run the circuit and get result distribution

job = sampler.run(circuit)

quasi_dist = job.result().quasi_dists[0]

print(quasi_dist)

Copy

from qiskit.primitives import Estimator

estimator = Estimator()


# Specify problem hamiltonian

from qiskit.quantum_info import SparsePauliOp


hamiltonian = SparsePauliOp.from_list([

   ("II", -1.052373245772859),

   ("IZ", 0.39793742484318045),

   ("ZI", -0.39793742484318045),

   ("ZZ", -0.01128010425623538),

   ("XX", 0.18093119978423156)

])


# Define VQE ansatz, initial point and cost function

from qiskit.circuit.library import TwoLocal

ansatz = TwoLocal(num_qubits=2, rotation_blocks="ry", entanglement_blocks="cz")

initial_point = initial_point = [0] * 8


def cost_function(params, ansatz, hamiltonian, estimator):

    energy = estimator.run(ansatz, hamiltonian, parameter_values=params).result().values[0]

    return energy


# Run VQE using SciPy minimizer routine

from scipy.optimize import minimize

result = minimize(cost_function, initial_point, args=(ansatz, hamiltonian, estimator), method="cobyla")


# Print minimum eigenvalue

print(result.fun)

Copy


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