Quantum Computing for Developers: Getting Started with Qiskit and IBM

Published on September 8, 2025 By Sundarapandi Muthupandi

Quantum computing represents one of the most significant technological frontiers of our time. For software developers, understanding quantum computing principles and learning to program quantum systems is becoming increasingly valuable as the technology moves from research labs toward practical applications.

Understanding Quantum Computing Fundamentals

Unlike classical computers that use bits (0 or 1), quantum computers use quantum bits (qubits) that can exist in superposition, representing both 0 and 1 simultaneously. This fundamental difference enables quantum computers to process certain types of problems exponentially faster than classical computers.

Key Quantum Concepts for Developers

Qubits and Superposition

Qubits are the fundamental units of quantum information. Unlike classical bits, qubits can exist in a superposition of states, allowing quantum computers to explore multiple solutions simultaneously.

Entanglement

Quantum entanglement creates correlations between qubits that persist regardless of distance. This property enables quantum algorithms to process information in ways impossible with classical computers.

Quantum Gates

Quantum gates are the building blocks of quantum circuits, analogous to logic gates in classical computing. They manipulate qubits through unitary operations.

Measurement and Collapse

Measuring a quantum system causes the superposition to collapse to a definite state. This measurement process is probabilistic and irreversible.

Introduction to Qiskit

Qiskit is IBM's open-source quantum computing framework that enables developers to create, simulate, and run quantum programs. It provides tools for quantum circuit design, optimization, and execution on both simulators and real quantum hardware.

Setting Up Your Quantum Development Environment

Installing Qiskit

# Install Qiskit and visualization tools
pip install qiskit[visualization]
pip install qiskit-aer
pip install qiskit-ibm-runtime

# For Jupyter notebook support
pip install jupyter matplotlib

IBM Quantum Account Setup

# Save your IBM Quantum token
from qiskit_ibm_runtime import QiskitRuntimeService

# Save account (one-time setup)
QiskitRuntimeService.save_account(
    channel="ibm_quantum",
    token="YOUR_IBM_QUANTUM_TOKEN"
)

# Load account for future use
service = QiskitRuntimeService()

Your First Quantum Circuit

Creating a Simple Quantum Circuit

from qiskit import QuantumCircuit, transpile
from qiskit_aer import AerSimulator
from qiskit.visualization import plot_histogram
import matplotlib.pyplot as plt

# Create a quantum circuit with 2 qubits and 2 classical bits
qc = QuantumCircuit(2, 2)

# Apply Hadamard gate to first qubit (creates superposition)
qc.h(0)

# Apply CNOT gate (creates entanglement)
qc.cx(0, 1)

# Measure both qubits
qc.measure([0, 1], [0, 1])

# Display the circuit
print(qc.draw())

# Run on simulator
simulator = AerSimulator()
compiled_circuit = transpile(qc, simulator)
result = simulator.run(compiled_circuit, shots=1024).result()
counts = result.get_counts()

# Plot results
plot_histogram(counts)
plt.show()

Understanding the Results

This circuit creates a Bell state, demonstrating quantum entanglement. The measurement results show only '00' and '11' outcomes, never '01' or '10', proving the qubits are entangled.

Quantum Gates and Operations

Single-Qubit Gates

# Common single-qubit gates
qc = QuantumCircuit(1)

# Pauli gates
qc.x(0)  # X gate (bit flip)
qc.y(0)  # Y gate 
qc.z(0)  # Z gate (phase flip)

# Hadamard gate (creates superposition)
qc.h(0)

# Rotation gates
qc.rx(np.pi/4, 0)  # Rotation around X-axis
qc.ry(np.pi/3, 0)  # Rotation around Y-axis
qc.rz(np.pi/2, 0)  # Rotation around Z-axis

# Phase gate
qc.s(0)  # S gate (π/2 phase)
qc.t(0)  # T gate (π/4 phase)

Two-Qubit Gates

# Two-qubit gates
qc = QuantumCircuit(2)

# CNOT gate (controlled-X)
qc.cx(0, 1)  # Control: qubit 0, Target: qubit 1

# Controlled-Z gate
qc.cz(0, 1)

# Controlled rotation gates
qc.cry(np.pi/4, 0, 1)  # Controlled Y rotation

# SWAP gate
qc.swap(0, 1)

Quantum Algorithms for Developers

Quantum Teleportation

def quantum_teleportation():
    # Create circuit with 3 qubits and 3 classical bits
    qc = QuantumCircuit(3, 3)
    
    # Prepare the state to teleport (qubit 0)
    qc.ry(np.pi/4, 0)  # Arbitrary state
    
    # Create Bell pair between qubits 1 and 2
    qc.h(1)
    qc.cx(1, 2)
    
    # Bell measurement on qubits 0 and 1
    qc.cx(0, 1)
    qc.h(0)
    qc.measure([0, 1], [0, 1])
    
    # Apply corrections based on measurement results
    qc.cx(1, 2)
    qc.cz(0, 2)
    
    # Measure the teleported state
    qc.measure(2, 2)
    
    return qc

# Run the teleportation circuit
teleport_circuit = quantum_teleportation()
print(teleport_circuit.draw())

Quantum Fourier Transform

def qft(circuit, n):
    """Apply Quantum Fourier Transform to n qubits"""
    if n == 0:
        return circuit
    
    n -= 1
    circuit.h(n)
    
    for qubit in range(n):
        circuit.cp(np.pi/2**(n-qubit), qubit, n)
    
    qft(circuit, n)

def qft_circuit(n_qubits):
    qc = QuantumCircuit(n_qubits)
    qft(qc, n_qubits)
    
    # Reverse qubit order
    for qubit in range(n_qubits//2):
        qc.swap(qubit, n_qubits-qubit-1)
    
    return qc

# Create 3-qubit QFT circuit
qft_3 = qft_circuit(3)
print(qft_3.draw())

Grover's Search Algorithm

def grovers_algorithm(marked_item, n_qubits):
    """Grover's algorithm for searching unsorted database"""
    
    qc = QuantumCircuit(n_qubits, n_qubits)
    
    # Initialize superposition
    for qubit in range(n_qubits):
        qc.h(qubit)
    
    # Number of iterations
    iterations = int(np.pi/4 * np.sqrt(2**n_qubits))
    
    for _ in range(iterations):
        # Oracle: mark the target item
        oracle(qc, marked_item, n_qubits)
        
        # Diffusion operator
        diffusion(qc, n_qubits)
    
    # Measure all qubits
    qc.measure_all()
    
    return qc

def oracle(qc, marked_item, n_qubits):
    """Oracle function to mark the target item"""
    # Convert marked_item to binary and flip corresponding qubits
    for i, bit in enumerate(format(marked_item, f'0{n_qubits}b')):
        if bit == '0':
            qc.x(i)
    
    # Multi-controlled Z gate
    qc.h(n_qubits-1)
    qc.mcx(list(range(n_qubits-1)), n_qubits-1)
    qc.h(n_qubits-1)
    
    # Flip qubits back
    for i, bit in enumerate(format(marked_item, f'0{n_qubits}b')):
        if bit == '0':
            qc.x(i)

def diffusion(qc, n_qubits):
    """Diffusion operator (inversion about average)"""
    for qubit in range(n_qubits):
        qc.h(qubit)
        qc.x(qubit)
    
    qc.h(n_qubits-1)
    qc.mcx(list(range(n_qubits-1)), n_qubits-1)
    qc.h(n_qubits-1)
    
    for qubit in range(n_qubits):
        qc.x(qubit)
        qc.h(qubit)

# Search for item 3 in 3-qubit space
grover_circuit = grovers_algorithm(3, 3)
print(f"Grover's circuit for finding item 3:")
print(grover_circuit.draw())

Running on Real Quantum Hardware

Accessing IBM Quantum Systems

from qiskit_ibm_runtime import QiskitRuntimeService, Sampler

# Load your account
service = QiskitRuntimeService()

# Get available backends
backends = service.backends()
print("Available backends:")
for backend in backends:
    print(f"- {backend.name}: {backend.num_qubits} qubits")

# Select a backend
backend = service.backend('ibm_brisbane')  # Example backend

# Transpile circuit for the specific backend
transpiled_circuit = transpile(qc, backend, optimization_level=3)

# Run on quantum hardware
sampler = Sampler(backend)
job = sampler.run([transpiled_circuit], shots=1024)
result = job.result()

print(f"Job ID: {job.job_id()}")
print(f"Results: {result[0].data.meas.get_counts()}")

Error Mitigation

from qiskit_aer.noise import NoiseModel
from qiskit.providers.fake_provider import FakeMontreal

# Create a noise model from real device
fake_backend = FakeMontreal()
noise_model = NoiseModel.from_backend(fake_backend)

# Run with noise simulation
noisy_simulator = AerSimulator(noise_model=noise_model)
noisy_result = noisy_simulator.run(transpiled_circuit, shots=1024).result()

# Compare results
print("Ideal results:", ideal_counts)
print("Noisy results:", noisy_result.get_counts())

Quantum Machine Learning

Variational Quantum Eigensolvers (VQE)

from qiskit_algorithms import VQE
from qiskit_algorithms.optimizers import SPSA
from qiskit.circuit.library import TwoLocal
from qiskit.quantum_info import SparsePauliOp

# Define a Hamiltonian
hamiltonian = SparsePauliOp.from_list([
    ("II", 1.0),
    ("ZZ", 0.5),
    ("XX", 0.5)
])

# Create ansatz circuit
ansatz = TwoLocal(2, 'ry', 'cz', reps=2)

# Set up VQE
optimizer = SPSA(maxiter=100)
vqe = VQE(ansatz, optimizer, sampler=sampler)

# Run VQE
result = vqe.compute_minimum_eigenvalue(hamiltonian)
print(f"Ground state energy: {result.eigenvalue}")
print(f"Optimal parameters: {result.optimal_parameters}")

Quantum Neural Networks

from qiskit_machine_learning.neural_networks import CircuitQNN
from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier

# Create a quantum neural network
feature_map = TwoLocal(2, 'ry', 'cz', reps=1)
ansatz = TwoLocal(2, 'ry', 'cz', reps=2)

qnn = CircuitQNN(
    circuit=feature_map.compose(ansatz),
    input_params=feature_map.parameters,
    weight_params=ansatz.parameters,
    sampler=sampler
)

# Create classifier
classifier = NeuralNetworkClassifier(qnn, optimizer=optimizer)

# Train on quantum data
X_train = [[0, 0], [0, 1], [1, 0], [1, 1]]
y_train = [0, 1, 1, 0]  # XOR problem

classifier.fit(X_train, y_train)

# Make predictions
predictions = classifier.predict(X_train)
print(f"Predictions: {predictions}")
print(f"Accuracy: {classifier.score(X_train, y_train)}")

Quantum Cryptography and Security

Quantum Key Distribution (BB84)

import random

def bb84_protocol(n_bits):
    """Simulate BB84 quantum key distribution protocol"""
    
    # Alice generates random bits and bases
    alice_bits = [random.randint(0, 1) for _ in range(n_bits)]
    alice_bases = [random.randint(0, 1) for _ in range(n_bits)]
    
    # Alice prepares qubits
    alice_qubits = []
    for bit, base in zip(alice_bits, alice_bases):
        qc = QuantumCircuit(1, 1)
        
        if bit == 1:
            qc.x(0)  # Prepare |1⟩
        
        if base == 1:
            qc.h(0)  # Use + basis
        
        alice_qubits.append(qc)
    
    # Bob chooses random measurement bases
    bob_bases = [random.randint(0, 1) for _ in range(n_bits)]
    
    # Bob measures in his chosen bases
    bob_results = []
    for qc, base in zip(alice_qubits, bob_bases):
        if base == 1:
            qc.h(0)  # Measure in + basis
        
        qc.measure(0, 0)
        
        # Simulate measurement
        simulator = AerSimulator()
        result = simulator.run(qc, shots=1).result()
        counts = result.get_counts()
        bob_results.append(int(list(counts.keys())[0]))
    
    # Public comparison of bases
    shared_key = []
    for i in range(n_bits):
        if alice_bases[i] == bob_bases[i]:
            shared_key.append(alice_bits[i])
    
    return alice_bits, alice_bases, bob_results, bob_bases, shared_key

# Run BB84 protocol
alice_bits, alice_bases, bob_results, bob_bases, key = bb84_protocol(20)
print(f"Shared key length: {len(key)}")
print(f"Shared key: {key}")

Optimization and Performance

Circuit Optimization

from qiskit import transpile
from qiskit.transpiler import PassManager
from qiskit.transpiler.passes import Optimize1qGatesDecomposition

# Optimize circuit depth and gate count
def optimize_circuit(circuit, backend):
    # Level 3 optimization
    optimized = transpile(
        circuit, 
        backend=backend, 
        optimization_level=3,
        seed_transpiler=42
    )
    
    print(f"Original: {circuit.depth()} depth, {circuit.size()} gates")
    print(f"Optimized: {optimized.depth()} depth, {optimized.size()} gates")
    
    return optimized

# Custom optimization pass
pass_manager = PassManager([Optimize1qGatesDecomposition(basis=['u3'])])
custom_optimized = pass_manager.run(circuit)

Error Mitigation Techniques

from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter

# Calibration circuits for error mitigation
cal_circuits, state_labels = complete_meas_cal(qubits=[0, 1])

# Run calibration
cal_results = simulator.run(cal_circuits, shots=1024).result()

# Create measurement filter
meas_fitter = CompleteMeasFitter(cal_results, state_labels)
meas_filter = meas_fitter.filter

# Apply error mitigation
mitigated_results = meas_filter.apply(raw_results)
print("Mitigated results:", mitigated_results.get_counts())

Industry Applications

Financial Modeling

Quantum computing shows promise for portfolio optimization, risk analysis, and fraud detection in financial services.

Drug Discovery

Quantum simulation of molecular interactions could accelerate pharmaceutical research and drug development.

Supply Chain Optimization

Quantum algorithms can solve complex optimization problems in logistics and supply chain management.

Cryptography and Security

Post-quantum cryptography development and quantum-safe security protocols are critical for future cybersecurity.

Learning Resources and Next Steps

Essential Learning Path

  • Master linear algebra and complex numbers
  • Understand quantum mechanics basics
  • Practice with Qiskit tutorials and textbook
  • Implement classic quantum algorithms
  • Explore quantum machine learning
  • Contribute to open-source quantum projects

Recommended Resources

  • Qiskit Textbook (online interactive course)
  • IBM Quantum Experience
  • Microsoft Quantum Development Kit
  • Google Cirq framework
  • Quantum Computing Stack Exchange

Career Opportunities

Emerging Roles

Quantum software engineer, quantum algorithm developer, quantum machine learning researcher, and quantum systems architect are becoming viable career paths.

Skills in Demand

Quantum programming, algorithm design, error correction, optimization, and hybrid classical-quantum systems development.

The Future of Quantum Development

Quantum computing is transitioning from research to practical applications. As quantum hardware improves and error rates decrease, developers who understand quantum programming will be positioned to tackle problems impossible for classical computers.

Challenges and Limitations

Current Limitations

Quantum computers today are noisy intermediate-scale quantum (NISQ) devices with limited coherence times and high error rates.

Programming Challenges

Quantum programming requires thinking differently about computation, embracing probabilistic outcomes and quantum mechanical principles.

Conclusion

Quantum computing represents a paradigm shift in computation that every developer should understand. While current quantum computers are limited, learning quantum programming with tools like Qiskit prepares you for the quantum advantage era.

Start experimenting with quantum circuits, implement classical algorithms, and build intuition for quantum concepts. The quantum revolution is coming, and developers who understand both classical and quantum computing will shape the future of technology.