Quantum enviroments

There are different frameworks or programming languages or tools to perform quantum algorithms, of these we will highlight five:

  • Qiskit
  • Cirq
  • QDK
  • Silq
  • Strawberry Fields

Installation Guide


For each of the frameworks or programming languages, the official links are mentioned so that they can be installed on different systems.


Qiskit can be installed on Windows7 or higher, Linux, and macOS 10.12.6 or later operating systems.



Cirq can be installed on Linux, Mac OS X and Windows operating systems, as well as on the Docker.



QDK has four ways to work: develop with Q# command line applications, develop with Q# Jupyter Notebooks, develop with Q# and Python - Enables you to combine Python and Q# and develop with Q# and .NET.



Silq can be installed on Linux, macOS and Windows.


Strawberry Fields

Strawberry Fields is a full-stack Python library for designing, optimizing, and utilizing photonic quantum computers.


Hello World

To verify the installation of each of these programming languages, you can check their operation by generating the program “Hello, World!”


qiskit qat.png

Use python to programming a qubit with respective its measurement (Considering as base the if the qiskit community).

import qiskit # call the qiskit's module

qr = qiskit.QuantumRegister(1) # call a quantum bit (or qubit)
cr = qiskit.ClassicalRegister(1) # call a clasical bit
program = qiskit.QuantumCircuit(qr, cr) # The quantum circuit is generated from the previous qubit and bit

Having the circuit design, the qubit measurement is performed.

program.measure(qr,cr) # The qubit is measured and stored in the classic bit.
<qiskit.circuit.instructionset.InstructionSet at 0x7fa7c815cb20>

Exist different forms to display the circuit as shown below

q0_0: ┤M├
c0_0: ═╩═
%matplotlib inline


The simulation does nothing but measure the qubit in its base state 0>, which is the value that is set as the standard for each qubit.

Circuit simulation is performed by selecting a backend as ‘qasm_simulator

job = qiskit.execute( program, qiskit.BasicAer.get_backend('qasm_simulator') )

When no modification is made to the qubit, its value will always be the default value which is 0.

print( job.result().get_counts() )
{'0': 1024}
Another way to identify the output values from a histogram that will display the total of measurements in the 0> state


qiskit qat2.png

Qiskit gives the opportunity to use quantum computers remotely, to access them you need to have an account at, where by checking the section “my account” you will get a token to use a eal quantum computer. In case that you save the count in your local computer is not neccesary do it again.

qiskit.IBMQ.save_account('my_token') #Replace the text my_token for your own token
configrc.store_credentials:WARNING:2020-08-23 00:46:18,646: Credentials already present. Set overwrite=True to overwrite.

After having the key saved, use the load method and select a computer (the list can be found at the link, for this example the ibmq16_melbourne (has 15 qubits) is selected.

qiskit.IBMQ.load_account() # load the token
provider = qiskit.IBMQ.get_provider('ibm-q') # select the provider
backend = provider.get_backend('ibmq_16_melbourne') # select the name of the quatum computer to use
print("real device:",
job = qiskit.execute( program, backend )

real device: ibmq_16_melbourne
print( job.result().get_counts() ) # show the result
{'1': 6, '0': 1018}


So ends the Qiskit Hello World! program in simulation and on a real computer.


cirq qat.png

The Hello, World! from Cirq is using python to programming a qubit by applying a square root of X with its respective measurement (Considering as base the if the qiskit community). The qubits in cirq are selected from a grid.

import cirq # call  the cirq's module
qubit = cirq.GridQubit(0, 0)# Select a qubit in the state |0>.

The Hello, World! circuit is generated

cirq qat2.png

# Create a circuit
circuit = cirq.Circuit()

circuit.append(cirq.X(qubit)**0.5)  # Apply Square root of NOT to the qubit.
circuit.append( cirq.measure(qubit, key='m'))  #Apply the measurement on the qubit.
print(circuit) # the circuit is displayed
(0, 0): ───X^0.5───M('m')───

The simulation of the previous circuit is generated. The results is the output value of the measurement.

simulator = cirq.Simulator() # Simulate the circuit.
repetitions= 20
result =, repetitions=repetitions) # run the simulation fo the circuit in 20 times
print(result)  # show the output values  of 'm'

Can be show in a histogram with the method plot_state_histogram()

counts = cirq.plot_state_histogram(result) # cal the method to generate a plot


In case to the the probability per state is posible with the coutns divided by the repetitions value

print("Probabiity =", counts/repetitions)  # cal the method to generate a plot
Probabiity = [0.5 0.5]

So ends the Cirq Hello World! program in simulation.


qdk qat.png

This example is based on the programming language Q# , which has the same syntax as C#.

As a first step you should consider that Q# has a great diversity of modules that are already based to perform gate operations like qubit measurement (like the data type qubit).

open Microsoft.Quantum.Intrinsic;     // for the H operation
open Microsoft.Quantum.Measurement;   // for MResetZ

operation MeasureSuperposition() : Result { // type Result return the measurement
    using (q = Qubit()) {  // using a variable of type qubit q
        H(q);              // apply Hadamard gate on the variable q
        return MResetZ(q); // apply the measurement on q
/snippet:(1,90): warning QS6003: The namespace is already open.
  • MeasureSuperposition

qdk qat2.png

After generating the function it is called from the command %simulate followed by the name of the function and showing the result of the simulation

%simulate MeasureSuperposition

So ends the QDK Hello World! program in simulation.


It is a new high-level language that is based on any program following the quantum computing paradigm.Being a new programming language it is very limited so there is no kernel for jupyter. In other words, the following code must be used inside a file with the extension slq.

silq qat.png

Silq is based on a mixture between c++ and python, where a main function must be defined and this must return the measurement of the qubits used in the program that was performed.

To represent the state 0 is the value false and 1 is true for this example so you can initialize the qubit in false, also, the basic gates of the quantum computation already come predefined as it is the case of Hadamard’s gate (which generates the superposition between the states 0 and 1).

”:= “ is the symbol that is represented to assign a value to a variable.

def main(){ // define the main function
    x:=H(false); // assign to the boolean variable x the superposition of the state false or 0
     return measure(x); // return the

Generate a file with the previous code and save as hello_world.slq.

The expect output


For future programs made in silq, the link will be shared with the source code file.

silq qat2.png

Strawberry Fields

strawberryFields qat.png

The program is started by calling the strawberryfields modules that are in charge of generating the qubits, the circuit and the simulation.

import strawberryfields as sf
from strawberryfields import ops

There are different gates in this library to make quantum circuits such as Sgate and BSgate.

For more information about the gates it uses you can find the following link

# create a quantum program with 3 qubits
prog = sf.Program(3)

# describe the circuit
with prog.context as q:
    ops.Sgate(0.54) | q[0] # call the Sgate
    ops.Sgate(0.54) | q[1]
    ops.Sgate(0.54) | q[2]
    ops.BSgate(0.43, 0.1) | (q[0], q[2]) # call the BSGate
    ops.BSgate(0.43, 0.1) | (q[1], q[2])
    ops.MeasureFock() | q # perform the measurement on all qubits of the variable q

strawberryFields qat2.png

It is possible to print the set of instructions that were made to build the circuit as the measurements that will be made at the end of it.

Sgate(0.54, 0) | (q[0])
Sgate(0.54, 0) | (q[1])
Sgate(0.54, 0) | (q[2])
BSgate(0.43, 0.1) | (q[0], q[2])
BSgate(0.43, 0.1) | (q[1], q[2])
MeasureFock | (q[0], q[1], q[2])

The Engine method works with the name of the backend and the possible options for that backend.

You can work with three backends.

  • The 'fock' written in NumPy.
  • The 'gaussian' written in NumPy.
  • The 'tf' written in TensorFlow 2 (for this one require install tensorflow 2).

After indicating the backend, the run method is implemented on the variable that the circuit is

eng = sf.Engine("fock", backend_options={"cutoff_dim": 5})
result =

It is possible to print the description of the output status

<FockState: num_modes=3, cutoff=5, pure=True, hbar=2>

Is also possible identify trace of the quantum state

state = result.state

It has the property of identifying the density matrix of the output state. # density matrix
(5, 5, 5, 5, 5, 5)

Finally, is possible to measurement samples from any measurements performed.

array([[0, 0, 0]])

With this fnish the Hello, World! of strawberries fields.


If anyone needs help on installing or question about the code, we can try to help in our discord server