Quantum computing is an emerging and promising technology that leverages quantum mechanics to vastly increase computing power. However, it also introduces many new challenges compared to classical computing that we are familiar with. Python has become a popular programming language for quantum computing due to its versatility and wide adoption in data science. This makes it a convenient gateway for researchers and programmers to explore quantum computing applications.
This article provides a practical guide to integrating Python with quantum computing. It discusses popular quantum computing software development kits (SDKs) that interface with Python and how to get started with quantum algorithm development. Examples will be given of building quantum circuits and running simulations and experiments on real quantum hardware. The goal is to equip readers with hands-on knowledge of using Python for quantum programming.
Setting Up Development Environment
There are a few leading quantum computing SDKs that support Python including Qiskit, Cirq, and PennyLane. Qiskit is developed by IBM for their quantum hardware and simulators. Cirq is an open-source toolkit maintained by Google. PennyLane focuses on differentiable programming for quantum machine learning.
To get set up, first install the chosen SDK via pip. For example, to install Qiskit:
pip install qiskit
Next, import the necessary modules from the SDK. Qiskit code would look like:
from qiskit import *
An SDK like Qiskit also requires setting up credentials to access IBM's quantum hardware and simulators in the cloud. Overall, the installation and setup is quite straightforward for any of the Python-based SDKs.
Building Quantum Circuits
At the core of quantum programming is building quantum circuits - the blueprint for quantum operations like gates and measurements. In Python SDKs, circuits are represented as objects that can be modified, combined, and analyzed programmatically.
For example, in Qiskit a simple circuit applying a Hadamard gate to a qubit can be:
from qiskit import *
circuit = QuantumCircuit(1, 1)
circuit.h(0)
circuit.measure([0], [0])
Circuits can be visualized, composed of multiple gates, and run on simulators or real hardware all from Python. This interactive development process is highly convenient compared to lower-level hardware description languages.
Simulations and Hardware Execution
Quantum simulators allow testing and debugging of quantum algorithms on classical hardware before running on limited quantum processors. All major Python SDKs provide high-performance simulators.
In Qiskit, a circuit can be simulated using the Aer simulator backend:
from qiskit import QuantumCircuit, ClassicalRegister, execute
from qiskit.providers.aer import QasmSimulator
# Define circuit etc
result = execute(circuit, QasmSimulator()).result()
counts = result.get_counts(circuit)
To run circuits on actual quantum processors, credentials need to be added to access cloud-hosted hardware backends in SDKs like Qiskit. Programs are compiled, scheduled and executed remotely. Python provides controls to optimize execution and review performance results.
Quantum Algorithms
Beyond basic circuit building, Python SDKs enable implementing various quantum algorithms from the literature. For example, Grover's search algorithm can locate a target item in an unsorted database quadratically faster than classical algorithms:
# Define oracle circuit with satisfying assignment
# Apply Hadamard to initialize superposition
# Iterate Grover's operation (Walsh-Hadamard and oracle)
# Final measurement
Grover’s Search in Qiskit:
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute, Aer
qr = QuantumRegister(3)
cr = ClassicalRegister(3)
circuit = QuantumCircuit(qr, cr)
circuit.h(qr)
circuit.cx(qr[0], qr[1])
circuit.cx(qr[0], qr[2])
circuit.barrier()
circuit.x(qr[1]) # Action Oracle
circuit.h(qr[0])
circuit.draw(output='mpl', filename='grover.png')
result = execute(circuit, Aer.get_backend('qasm_simulator')).result()
print(result.get_counts())
Variational Quantum Classifier in Pennylane:
dev = qml.device('default.qubit', wires=2)
@qml.qnode(dev)
def circuit(params, x):
... # Define variational circuit
return qml.expval(qml.PauliZ(0))
obs = []
for x in dataset:
obs.append(circuit(trained_params, x))
predictions = np.argmax(obs, axis=1)
Quantum Fourier Transform in Cirq:
qubits = [cirq.GridQubit(i, 0) for i in range(3)]
circuit = cirq.Circuit()
circuit.append(cirq.H.on_each(*qubits))
circuit.append(cirq.CNOT.on(qubits[0], qubits[1]))
circuit.append(cirq.CNOT.on(qubits[1], qubits[2]))
...
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=100)
Other popular algorithms supported include quantum Fourier transform, phase estimation, quantum machine learning models, and variational algorithms. Python streamlines the development workflow for theoretical and applied quantum algorithm research.
Differentiable Programming
A unique strength of Python SDKs like PennyLane is their support for differentiable programming techniques. This allows using tools like TensorFlow and PyTorch to optimize variational quantum circuits.
For example, a variational quantum eigensolver can be implemented to find the ground state of chemical Hamiltonians:
import pennylane as qml
dev = qml.device('default.qubit', wires=3)
@qml.qnode(dev)
def circuit(params):
# Parameterized circuit
return qml.expval(qml.PauliZ(0))
opt = qml.AdamOptimizer(stepsize=0.1)
params = qml.init.random_unitary(num_qubits, seed=1234)
loss = circuit(params)
for i in range(100):
params = opt.step(circuit, params)
loss = circuit(params)
This combines quantum computing with classical optimization - an important hybrid technique. Python's rich ecosystem plays a key role.
Example implementing the Variational Quantum Eigensolver (VQE) algorithm in Qiskit to find the ground state energy of the hydrogen molecule H2:
from qiskit import Aer, QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit.aqua.components.variational_forms import RYRZ
from qiskit.aqua import QuantumInstance
from qiskit.aqua.algorithms import VQE
from qiskit.chemistry.drivers import PySCFDriver
from qiskit.chemistry import FermionicOperator
# Define H2 molecule
driver = PySCFDriver(atom='H .0 .0; H .0 0.74',
basis='sto-3g')
# Generate qubit Hamiltonian
molecule = driver.run()
qm_op, qubit_mapping = FermionicOperator.map_fermionic_operator_to_qubit_operator(molecule.second_quantized_operator)
# Set up VQE algorithm
qr = QuantumRegister(qubit_mapping.n_qubits, 'q')
cr = ClassicalRegister(1, 'c')
circuit = QuantumCircuit(qr, cr)
var_form = RYRZ(qubit_mapping.n_qubits, reps=2)
var_form.apps(circuit)
backend = Aer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend, shots=1024, pass_manager=None)
algorithm = VQE(qubit_op, var_form, quantum_instance)
result = algorithm.run()
print('Ground state energy: {}'.format(result.eigenvalue))
This implements the full VQE workflow for the H2 molecule - problem specification, qubit mapping, variational ansatz, simulation and energy computation.
Conclusion
Python has established itself as a predominant programming language for exploring quantum applications due to its versatility across domains. Quantum SDKs like Qiskit, Cirq and PennyLane lower the barrier for users to tap into cloud-based quantum hardware and simulators interactively from Python.
This practical guide covered the basics of setting up development environments, building quantum circuits, simulating algorithms, interfacing with hardware backends, and implementing popular algorithms and differentiable approaches. Python streamlines quantum software development and integrates well with other classical tools. It will continue playing a central role as quantum technologies mature and proliferate in scope.
