Quantum Software Stack

Quantum Software Landscape

The quantum software stack includes:

1. Circuit-level frameworks: Qiskit, Cirq, Q#
2. Variational frameworks: PennyLane, TensorFlow Quantum
3. Cloud platforms: IBM Quantum, Amazon Braket, Azure Quantum
4. Compilers: t|ket>, Qiskit Transpiler, Cirq Optimizer
5. Simulators: statevector, density matrix, tensor network simulators

Qiskit

Qiskit (IBM) is the most widely used quantum SDK:

from qiskit import QuantumCircuit, Aer, execute

qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])

simulator = Aer.get_backend('qasm_simulator')
result = execute(qc, simulator, shots=1000).result()
print(result.get_counts())

Qiskit provides circuits, transpilation, execution on real hardware, and a rich ecosystem of algorithms.

Cirq

Cirq (Google) focuses on NISQ algorithms and hardware-aware compilation:

import cirq

q0, q1 = cirq.LineQubit.range(2)
circuit = cirq.Circuit([
    cirq.H(q0),
    cirq.CNOT(q0, q1),
    cirq.measure(q0, q1)
])
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1000)
print(result.histogram(key='q(0),q(1)'))

Cirq emphasizes control over gate placement and hardware topology.

PennyLane

PennyLane (Xanadu) is designed for quantum machine learning and variational algorithms:

import pennylane as qml
import numpy as np

dev = qml.device('default.qubit', wires=2)

@qml.qnode(dev)
def circuit(params):
    qml.RY(params[0], wires=0)
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(0))

params = np.array([0.5], requires_grad=True)
print(circuit(params))

PennyLane provides automatic differentiation, optimization, and integration with classical ML frameworks.

Q# (Microsoft)

Q# is a domain-specific language for quantum programming with integrated classical control:

operation BellState() : (Result, Result) {
    use q = Qubit[2];
    H(q[0]);
    CNOT(q[0], q[1]);
    return (M(q[0]), M(q[1]));
}

Q# emphasizes resource estimation, error correction, and long-term quantum computing vision.

Comparison

Python: Framework Comparison

# Qiskit style
def qiskit_bell():
    from qiskit import QuantumCircuit
    qc = QuantumCircuit(2, 2)
    qc.h(0); qc.cx(0, 1); qc.measure([0,1], [0,1])
    return qc

# Cirq style
def cirq_bell():
    import cirq
    q0, q1 = cirq.LineQubit.range(2)
    return cirq.Circuit([cirq.H(q0), cirq.CNOT(q0, q1), cirq.measure(q0, q1)])

# PennyLane style
def pennylane_bell():
    import pennylane as qml
    dev = qml.device('default.qubit', wires=2, shots=1000)
    @qml.qnode(dev)
    def circuit():
        qml.Hadamard(wires=0)
        qml.CNOT(wires=[0, 1])
        return qml.probs(wires=[0, 1])
    return circuit()

print("Qiskit:", qiskit_bell())
print("Cirq:", cirq_bell())
print("PennyLane probs:", pennylane_bell())