Quantum Circuits

qiskit.circuit

Overview

The fundamental element of quantum computing is the quantum circuit. A quantum circuit is a computational routine consisting of coherent quantum operations on quantum data, such as qubits. It is an ordered sequence of quantum gates, measurements and resets, which may be conditioned on real-time classical computation. A set of quantum gates is said to be universal if any unitary transformation of the quantum data can be efficiently approximated arbitrarily well as a sequence of gates in the set. Any quantum program can be represented by a sequence of quantum circuits and classical near-time computation.

In Qiskit, this core element is represented by the QuantumCircuit class. Below is an example of a quantum circuit that makes a three-qubit GHZ state defined as:

from qiskit import QuantumCircuit
# Create a circuit with a register of three qubits
circ = QuantumCircuit(3)
# H gate on qubit 0, putting this qubit in a superposition of |0> + |1>.
circ.h(0)
# A CX (CNOT) gate on control qubit 0 and target qubit 1 generating a Bell state.
circ.cx(0, 1)
# CX (CNOT) gate on control qubit 0 and target qubit 2 resulting in a GHZ state.
circ.cx(0, 2)
# Draw the circuit
circ.draw('mpl')

Supplementary Information

Quantum Circuit with conditionals

When building a quantum circuit, there can be interest in applying a certain gate only if a classical register has a specific value. This can be done with the InstructionSet.c_if() method.

In the following example, we start with a single-qubit circuit formed by only a Hadamard gate (HGate), in which we expect to get $|0\rangle$ and $|1\rangle$ with equal probability.

from qiskit import BasicAer, transpile, QuantumRegister, ClassicalRegister, QuantumCircuit
 
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
qc.h(0)
qc.measure(0, 0)
qc.draw('mpl')

backend = BasicAer.get_backend('qasm_simulator')
tqc = transpile(qc, backend)
counts = backend.run(tqc).result().get_counts()
 
print(counts)

{'0': 524, '1': 500}

Now, we add an XGate only if the value of the ClassicalRegister is 0. That way, if the state is $|0\rangle$, it will be changed to $|1\rangle$ and if the state is $|1\rangle$, it will not be changed at all, so the final state will always be $|1\rangle$.

from qiskit import BasicAer, transpile, QuantumRegister, ClassicalRegister, QuantumCircuit
 
qr = QuantumRegister(1)
cr = ClassicalRegister(1)
qc = QuantumCircuit(qr, cr)
qc.h(0)
qc.measure(0, 0)
 
qc.x(0).c_if(cr, 0)
qc.measure(0, 0)
 
qc.draw('mpl')

backend = BasicAer.get_backend('qasm_simulator')
tqc = transpile(qc, backend)
counts = backend.run(tqc).result().get_counts()
 
print(counts)

{'1': 1024}

Quantum Circuit Properties

When constructing quantum circuits, there are several properties that help quantify the “size” of the circuits, and their ability to be run on a noisy quantum device. Some of these, like number of qubits, are straightforward to understand, while others like depth and number of tensor components require a bit more explanation. Here we will explain all of these properties, and, in preparation for understanding how circuits change when run on actual devices, highlight the conditions under which they change.

Consider the following circuit:

from qiskit import QuantumCircuit
qc = QuantumCircuit(12)
for idx in range(5):
   qc.h(idx)
   qc.cx(idx, idx+5)
 
qc.cx(1, 7)
qc.x(8)
qc.cx(1, 9)
qc.x(7)
qc.cx(1, 11)
qc.swap(6, 11)
qc.swap(6, 9)
qc.swap(6, 10)
qc.x(6)
qc.draw('mpl')

From the plot, it is easy to see that this circuit has 12 qubits, and a collection of Hadamard, CNOT, X, and SWAP gates. But how to quantify this programmatically? Because we can do single-qubit gates on all the qubits simultaneously, the number of qubits in this circuit is equal to the width of the circuit:

qc.width()

12

We can also just get the number of qubits directly:

qc.num_qubits

12

It is also straightforward to get the number and type of the gates in a circuit using QuantumCircuit.count_ops():

qc.count_ops()

OrderedDict([('cx', 8), ('h', 5), ('x', 3), ('swap', 3)])

We can also get just the raw count of operations by computing the circuits QuantumCircuit.size():

qc.size()

19

A particularly important circuit property is known as the circuit depth. The depth of a quantum circuit is a measure of how many “layers” of quantum gates, executed in parallel, it takes to complete the computation defined by the circuit. Because quantum gates take time to implement, the depth of a circuit roughly corresponds to the amount of time it takes the quantum computer to execute the circuit. Thus, the depth of a circuit is one important quantity used to measure if a quantum circuit can be run on a device.

The depth of a quantum circuit has a mathematical definition as the longest path in a directed acyclic graph (DAG). However, such a definition is a bit hard to grasp, even for experts. Fortunately, the depth of a circuit can be easily understood by anyone familiar with playing Tetris. Lets see how to compute this graphically:

We can verify our graphical result using QuantumCircuit.depth():

qc.depth()

9

Quantum Circuit API

Quantum Circuit Construction

Gates and Instructions

Control Flow Operations

The SwitchCaseOp also understands a special value:

A special object that represents the “default” case of a switch statement. If you use this as a case target, it must be the last case, and will match anything that wasn’t already matched. For example:

from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.circuit import SwitchCaseOp, CASE_DEFAULT
 
body0 = QuantumCircuit(2, 2)
body0.x(0)
body1 = QuantumCircuit(2, 2)
body1.z(0)
body2 = QuantumCircuit(2, 2)
body2.cx(0, 1)
 
qr, cr = QuantumRegister(2), ClassicalRegister(2)
qc = QuantumCircuit(qr, cr)
qc.switch(cr, [(0, body0), (1, body1), (CASE_DEFAULT, body2)], qr, cr)

When using the builder interface of QuantumCircuit.switch(), this can also be accessed as the DEFAULT attribute of the bound case-builder object, such as:

from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
 
qr, cr = QuantumRegister(2), ClassicalRegister(2)
qc = QuantumCircuit(qr, cr)
with qc.switch(cr) as case:
    with case(0):
        qc.x(0)
    with case(1):
        qc.z(0)
    with case(case.DEFAULT):
        qc.cx(0, 1)

Parametric Quantum Circuits

Random Circuits

random_circuit

qiskit.circuit.random.random_circuit(num_qubits, depth, max_operands=4, measure=False, conditional=False, reset=False, seed=None)

GitHub

Generate random circuit of arbitrary size and form.

This function will generate a random circuit by randomly selecting gates from the set of standard gates in qiskit.extensions. For example:

from qiskit.circuit.random import random_circuit
 
circ = random_circuit(2, 2, measure=True)
circ.draw(output='mpl')

Parameters

• num_qubits (int) – number of quantum wires
• depth (int) – layers of operations (i.e. critical path length)
• max_operands (int) – maximum qubit operands of each gate (between 1 and 4)
• measure (bool) – if True, measure all qubits at the end
• conditional (bool) – if True, insert middle measurements and conditionals
• reset (bool) – if True, insert middle resets
• seed (int) – sets random seed (optional)

Returns

constructed circuit

Return type

QuantumCircuit

Raises

CircuitError – when invalid options given

Circuit Analysis

Annotated Operations

Exceptions

Almost all circuit functions and methods will raise a CircuitError when encountering an error that is particular to usage of Qiskit (as opposed to regular typing or indexing problems, which will typically raise the corresponding standard Python error).

CircuitError