Tutorial

Examples of how to use the package to convert different expressions, such as quantum circuits to matrices, matrices to quantum circuits, and quantum circuits to bra-ket notations.

It also shows how to pass options to the conversion service, such as expression simplification or expansion.

Before you start

from qiskit import QuantumCircuit, transpile
from qiskit_aer import AerSimulator

from qiskit_class_converter import ConversionService, ConversionType

Declare required qiskit_class_converter packages.

matrix to quantum circuit

input_value = [
[1, 0, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 0]
]

sample_converter = ConversionService(conversion_type="MATRIX_TO_QC", option={"label": "CX gate"})
result = sample_converter.convert(input_value=input_value)
quantum_circuit = QuantumCircuit(2, 2)
quantum_circuit.x(0)
quantum_circuit.append(result, [0, 1])
quantum_circuit.measure(range(2), range(2))
backend = AerSimulator()
qc_compiled = transpile(quantum_circuit, backend)
counts = backend.run(qc_compiled).result().get_counts()

You will get the variables : quantum_circuit, qc_compiled's counts

quantum_circuit

text : 
     ┌───┐┌──────────┐┌─┐   
q_0: ┤ X ├┤0         ├┤M├───
     └───┘│  CX gate │└╥┘┌─┐
q_1: ─────┤1         ├─╫─┤M├
          └──────────┘ ║ └╥┘
c: 2/══════════════════╩══╩═
                       0  1   

qc_compiled's counts

{'11': 1024}

quantum circuit to matrix

quantum_circuit = QuantumCircuit(2, 2)
quantum_circuit.x(0)
quantum_circuit.cx(0, 1)
sample_converter = ConversionService(conversion_type="QC_TO_MATRIX")
var = sample_converter.convert(input_value=quantum_circuit)

You will get the variables : var["gate"], var["name"], var["result"]

var["gate"]: gate matrix list values before are computed.

[array([[0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j],
       [1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],
       [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j]]),
 array([[1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],
       [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j],
       [0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j]])]

var["name"]: gate name list values before are computed.

[(0, ['I_{q1}', 'X_{q0}']), (1, ['CX_{q0, q1}'])]

var["result"]: final result is the calculated matrix.

array([[0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],
       [1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])

The raw option creates it as variable that can be used with latex syntax.

ConversionService(conversion_type="QC_TO_MATRIX", option={"print": "raw"})

quantum circuit to bra-ket

quantum_circuit = QuantumCircuit(2, 2)
quantum_circuit.h(0)
quantum_circuit.x(0)
quantum_circuit.cx(0, 1)
sample_converter = ConversionService(conversion_type="QC_TO_BRA_KET")
result = sample_converter.convert(input_value=quantum_circuit)

You will get the variables : result

(sqrt(2)/2)*|0> + (sqrt(2)/2)*|11>

The raw option creates it as variable that can be used with latex syntax.

ConversionService(conversion_type="QC_TO_BRA_KET", option={"print": "raw"})

You can also add options like below:

ConversionService(conversion_type="QC_TO_BRA_KET", option={"expression": "simplify"})

ConversionService(conversion_type="QC_TO_BRA_KET", option={"expression": "expand"})

string to bra-ket

sample_converter = ConversionService(conversion_type="STR_TO_BRA_KET")
result = sample_converter.convert(input_value="sqrt(2)*|00>/2+sqrt(2)*|11>/2")

You will get the (sympy) variables : result

type(result)

The raw option creates it as variable that can be used with latex syntax.

ConversionService(conversion_type="STR_TO_BRA_KET", option={"print": "raw"})

Options

convert method	option
QC_TO_BRA_KET	expression{simplify, expand}, print
QC_TO_MATRIX	print
MATRIX_TO_QC	label
STR_TO_BRA_KET	print

from qiskit_class_converter import ConversionService

ConversionService(conversion_type="QC_TO_BRA_KET", option={"expression": "simplify"})

Last modified: 07 November 2023